Digital-First has become the trend of organizations in the world and Latin America, in which a digital strategy is chosen first for product and service delivery, especially when a greater impact of the brand is sought more immediately to a certain segment of the market along with a wider dissemination on the offer, in a more customized way and, above all, if it seeks to get closer to the end customer. According to Marketing4Commerce, Digital Report, the number of internet users in the world reaches 5.16 billion (64.4% of the world’s population, as of 2023) with an internet browsing time greater than 6 hours, and people with mobile devices reach 5.44 billion (68% of the world’s population, as of 2023).

Also, we see this reflected in an Adobe report (Digital Trends 2023) in which more than 70% of organizations, both leaders and followers, believe that their customers’ expectations are constantly adjusted to align with improved omnichannel experiences, this is because end customers are constantly evaluating their experiences in comparison to their last best experience. Certainly, the most memorable experiences will be created by organizations that know how to leverage data and combine it with human knowledge to anticipate customer needs, with greater empathy and in a more individualized way. 

In this scenario, Artificial Intelligence (AI) becomes an ally to implement customer experience strategies in a customized and innovative way, taking advantage of voice recognition tools, understanding of natural language, data on behavior patterns and customer preferences. In recent years, interactions with virtual assistants have become commonplace, prompting the development of language models for certain tasks or expected outcomes. This is known as Prompt Engineering, which is the process of building alerts or inputs to guide a certain AI system behavior and get desired and accurate answers from AI models. So AI assumes a digital collaborator role that not only works as a point of contact with customers, but also boosts knowledge and productivity for the organization’s collaborators.

Content:

• What is Prompt Engineering?
• Prompt Engineering Technical Side
• Why Question Engineering Matters
• Evolution of Prompt Engineering
• The Art and Science of Creating Questions
• How Question Engineering Works
• The Role of a Question Engineer
• The Future of Prompt Engineering
• Challenges and Opportunities
• Conclusions

What is Prompt Engineering?

According to Techopedia, (Prompt Engineering) refers to a technique used in artificial intelligence (AI) to optimize and adjust language models for particular tasks and desired outcomes. Also known as Prompt design, which carefully builds prompts or inputs for AI models in order to improve their performance of specific tasks. Properly designed prompts are used to guide and modify the desired performance of the AI system and obtain accurate and desired responses from AI models.

Prompt Engineering uses the capabilities of language models and optimizes their results through properly designed prompts. This allows not only to rely on pre-training or fine-tuning, but also to help users guide models to specific goals by encouraging accurate responses and providing direct directions, exceptions, or examples in prompts.

According to a survey conducted by COPC Inc. During 2022, “Improving Customer Experience” reached 87% as the most mentioned goal in terms of implementing AI-based solutions. In this regard, 83% of respondents stated that they use AI-based solutions mainly for contact applications with their customers, and that AI has endless uses that directly impact Customer Experience. According to a study conducted by CX optimization 2023, the most implemented uses are content creation, customer profiling and reduction of internal calls.

Large Language Models, LLM),which are advanced linguistic models based on Deep Learning algorithms to process and analyze large amounts of text data. LLM works from artificial neural networks (systems that are inspired by the function of the human brain), which are trained to learn patterns and relationships in a text and generate answers to users’ questions. This enables LLM to be able to analyze large volumes of text data and from that, use that information to understand words, phrases and sentences and their contexts, enabling human-IA communication.

Prompt Engineering Technical Side

As we mentioned earlier, LLMs are trained from large amounts of text data to teach the model to recognize relationships and patterns in a text. All this data is processed to adjust model parameters and thereby improve language accuracy and understanding. Text preprocessing techniques (removal of irrelevant words, standardization of words to detect the variability of a text), hyperparameter adjustments and optimization are also used to achieve the highest model accuracy. To implement Prompt Engineering, there are several techniques, such as:

1. Zero-Shot Prompting: It generates a response without giving linguistic models examples or previous context. It is used for quick answers to general questions or topics.
   • Example: “What is a prompt?”
   • Answer: “A Prompt is…”
2. One-Shot Prompting: A response is retrieved from an example or context provided by the user.
   • Example: “If budget in Portuguese is orçamento, how can I translate Supplier?”
   • Answer: “Fornecedor”.
3.  Information Retrieval: Generative AI is asked a specific question for detailed answers. The key to this is the data source from which the LLMs are fed. For example, ChatGPT only has access to data after September 2021.
   • Example: “What are the advantages of LLM?”
   • Answer: “1. Content diversification. 2. Customization…”
4. Creative Writing: Through this technique it is possible to develop texts with imagination, stories and textual expressions that adapt to the preferences of the audience.
   • Example: “Write a poem to the moon”
   • Answer: “The moon can be taken in tablespoons…or as a capsule every two hours. It is good as a hypnotic and sedative and also relieves. “
5. Context Enrichment: Enrichment of information to Artificial Intelligence to improve understanding using methods 5W and 1H, (which refers to 5 W questions: Who, What, Where, When, Why); and 1 H, which is the How of a topic.
   • Example: “Is it good to eat Beets?”
   • Answer: “Who: Most people can eat Beet; What: “Beet or beetroot refers to an edible deep red tuber…”
6. Content Summary with a Specific Focus: It consists of directing the attention of AI to specific aspects of instruction, with a particular emphasis. It can be highlighted which elements should be prioritized in the model, so that the summary reflects the essence of the approach.
   • Example: “Full guide on website optimization techniques, but I only want the mobile optimization strategies.”
   • Answer: “The key aspects are: Mobile Cache – enabling this option allows…; List of Mobile User Agents – if the Mobile Cache feature is enabled…”
7. Fill Templates: To create versatile and structured content. A template with placeholders is used to customize prompts in different instructions while maintaining consistency. Content managers or web developers use this strategy to create custom content snippets, which have been generated by AI on their websites. An example is using a standard template for quoting, making AI fill in customer data, products, pricing, etc. Another example is automating custom emails from a template with a general structure – from the greeting, main text, farewell (‘Hello {Name}, Thank you for requesting our {Service}… {Close}.”
8.  Prompt Customization or Prompt Reframing: It allows you to change the wording of the questions while maintaining the original intent of the query. The language model can be designed to give multiple answers that respond to the original query in different ways. This can be done with synonyms or question rephrasing.
   • Example: “Original prompt: What are the ways to reduce network latency? Reworded Prompt: Can you list techniques to optimize network speed?”
9. Prompt Combination: It consists of merging different prompts or questions in the same instruction to obtain a complete answer.
   • Example: “Can you explain the differences between shared hosting and VPS hosting and recommend which one is better for a small e-commerce website?”
   • Answer: “Shared hosting and VPS hosting are two types of hosting services… Shared Hosting: …”
10. CChain-Of-Thought Prompting: It uses real-time AI interactions to guide toward more accurate and complete responses. It is not based on a single question, but on a sequence of questions or examples associated with it in order to elaborate on the original query. To do this, you need to divide a query or a complex topic into smaller sections. These parts are then presented as a sequence of queries that build on top of each other, to drive AI toward a desired answer.
    • Example: “What is the Main Theme?… For what purpose?… Who is the audience?…”
11. Iterative Prompting: It consists of making follow-up queries based on previous responses to dive into a certain topic, obtain additional information, or clarify any ambiguities about the initial result. This technique requires experts in (Natural Language Processing) to design iterative prompts and elaborate responses similar to those made by a human being.
    • Example: “What are the best movies of 2022?”
    • Answer: “‘Drive My Car’ by Ryûsuke Hamaguchi; ‘The Alley of Lost Souls’ by Guillermo del Toro; Martin McDonagh’s ‘The Banshees of Inisherin’; ‘Holy Spider’ by Ali Abbasi…”
12.  Interactive Storytelling & Role-Playing: It leverages AI’s ability to tailor responses based on previous prompts and interactions, developing a fluid narrative.
    • Example: “Prompt: I want to start a collaborative storytelling exercise with you. We will write a fantasy story about a land where magic exists,… The character will be….”
    • Answer: “In the shadows of a forest there was a…”
13. Implicit Information Injection: The particularity of this technique is that context is subtly given so that AI understands the needs without the need to express it explicitly.
    • Example: “Can you mention the best practices of Modernizing a Datacenter?”
    • Answer: “1- Raise the operating temperature of your data center; 2- Upgrade servers and systems for better consolidation and efficiency.”
14. Translation of Languages with Contextual Nuances: Generation of multilingual content, beyond translating words from one language to another, considering the cultural context or situation for a more accurate and natural translation.
    • Example: “Translate the sentence “She took the ball and ran with it” from English to French, bearing in mind that it is a business metaphor to refer to taking the reins of a project.”
    • Answer: “Elle a pris le ballon et a foncé avec”, considering the idea of taking the initiative of a project.”

In addition to these, we can mention Automatic Prompt Engineering (APE) as an advance in Artificial Intelligence that leverages LLMs to help AI automatically generate and select instructions on its own. The main steps are:

1. Assign the chatbot a specific task and show some examples.
2. The chatbot comes up with different ways to do the job, either by direct reasoning or by taking into account similar tasks that it knows.
3. These different methods are then tested in practice.
4. The chatbot assesses the effectiveness of each method.
5. AI will then choose a better method and apply it.

By means of Machine Learning, Generative AI tools can streamline tasks, from in-context data analysis to automated customer service, without the need for constant human-generated prompts.

It is worth mentioning that in Prompt Engineering it is important to consider basic technical aspects such as Temperature and what we call Top-K Sampling ,to improve the quality and diversity of AI-generated content, by influencing the model’s token (word or subword) selection process:

• Temperature: A higher temperature value (e.g., 1.0 or higher) will result in more diverse and creative text, while a lower value (e.g., 0.5 or lower) will produce more focused and deterministic results. To do this, it is recommended to encourage creativity based on higher temperature values when generating creative writing, brainstorming sessions or exploring innovative ideas. It is also recommended to improve coherence, opting for lower temperature values with well-structured, coherent and focused content, such as technical documentation or formal articles.
• Top-k sampling: is another recommended technique in AI text generation to control the model token selection process, from a restricted set of most likable k tokens. A smaller k value (e.g., 20 or 40) will result in more focused and deterministic text, while a larger k value (e.g., 100 or 200) will produce more diverse and creative results. Applications of top-k sampling include driving content diversity, using larger k-values when generating content that requires a wide range of ideas, perspectives, or vocabularies. It is also about ensuring focused results, choosing smaller k-values, generating content that requires a high degree of concentration, accuracy or consistency.

To implement the Temperature and Top-k Sampling techniques, Experimentation (testing multiple combinations of temperature and top-k values to identify the optimal configuration for tasks or contents) and Sequential Adjustments, are recommended, during the text generation process to control the performance of the AI model at different stages. For example, start with a high temperature and a large k-value to generate creative ideas, then switch to lower values for further refinement and focus.

Finally, it is recommended to apply the downward gradients which consist of an optimization algorithm to minimize an objective function and calculate the rate of change or gradient of the loss function. In Machine Learning this objective function is usually the loss function to evaluate the performance of the model. Parameters are updated iteratively using downward gradients until a local minimum is reached.

Why Question Engineering Matters

The speed with which OpenAI ChatGPT works since 2022 is overwhelming, today it is being used by millions of people, as a form of conversational artificial intelligence, based on advanced deep learning algorithms to understand human language.

Currently, organizations use multiple AI techniques such as Natural Language Processing, Question Engineering, Artificial Neural Network (NN), Machine Learning, and Markov Decision Processing, (MDP) to automate different tasks.

The importance of Question Engineering is that it improves the customer experience and interactions between people and AI, and contributes to building better conversational AI systems. These conversational AI systems dominate and will dominate the market in the coming years by using LLM in a consistent, relevant and accurate way. Just for reference, we have ChatGPT reaching 100 million active users within weeks of its launch.

For developers, Question Engineering helps to understand how AI-based models arrive at the expected answers and also obtain accurate information on how AI models work on the back-end. Of course, the development of prompts covering several topics and scenarios will be needed. Other benefits that you may mention are: that Question Engineering and the context of the text-image synthesis, allow to customize the features of the image (the style, the perspective, the aspect ratio, the point of view and the image resolution). It also plays an important role in the identification and mitigation of prompt injection attacks, thus protecting AI models from possible malicious activities.

Evolución de la Ingeniería de Preguntas

Natural Language Processing (NLP), is part of AI that helps perceive, as its name says, the “natural language” used by humans, enabling interaction between people and computers, thanks to its ability to understand words, phrases and sentences. It also includes syntactic (meaning of words and vocabulary) and semantic (comprehension within a sentence or combination of sentences) processing. The first lights of NLP were seen in the 1950s, when rule-based methods began to be adopted, consisting mostly of machine translation. Its application was in word/sentence analysis, answering questions and machine translation. Until the 1980s, computational grammar appeared as an active field of research. There was more availability of grammar tools and resources, which boosted their demand. Towards the 90s, the use of the web generated a large volume of knowledge, which boosted statistical learning methods that required working with NLP. In 2012 Deep Learning appeared as a solution for statistical learning, producing improvements in NLP systems, deepening raw data and learning from its attributes.

By 2019, the Generative Pre-trained Transformer (GPT) a remarkable advance in the domain of natural language processing emerged, as it is possible to pre-train large-scale language models to teach AI systems how to represent words and sentences in context. This enabled the development of machines that can understand and communicate using language in a manner very similar to that of humans. Its most popular application is ChatGPT, which obtains information from texts published since 2021 on the Internet, including news, encyclopedias, books, websites, among others, but lacks the ability to discriminate which information is true and which is not. Precisely for this reason, Question Engineering emerges as a method to optimize natural language processing in AI and improve the accuracy and quality of its answers.

The Art and Science of Creating Questions

A prompt is itself a text included in the Language Model (LM), and Question Engineering is the art of designing that text to get the desired result, with quality and accuracy. This involves tailoring data input so that AI-driven tools can understand user intent and get clear and concise answers. Which tells us that the process must be effective to ensure that AI-driven tools do not generate inappropriate and meaningless responses, especially when GPT solutions are based mostly on the frequency and association of words, and may yield incomplete or erroneous results.

To create Questions in Generative AI tools, it is recommended to follow this essential guide:

• Understanding the Desired Outcome
  

  Successful Prompt Engineering starts with knowing what questions to ask and how to do it effectively. So the user must be clear about what they want in the first place: objectives of the interaction and a clear outline of the expected results (what to get, for what audience and any associated actions that the system must perform).

•  Choose words carefully
  

  Like any computer system, AI tools can be precise in their use of commands and language, not knowing how to respond to unrecognized commands or language. It is recommended to avoid ambiguity, metaphors, idioms and specific jargon so as not to produce unexpected and undesirable results.

• Remember that form matters
  

  AI systems work based on simple, straightforward requests, through informal sentences and simple language. But complex requests will benefit from detailed, well-structured queries that adhere to a form or format consistent with the internal design of the system. This is essential in Prompt Engineering, as the shape and format may differ for each model, and some tools may have a preferred structure involving the use of keywords in predictable locations.

• Make clear and specific requests
  

  Consider that the system can only act on what it can interpret from a given message. So you have to make clear, explicit and actionable requests and understand the desired outcome. From there, work should then be done to describe the task to be performed or articulate the question to be answered.

• Pay attention to length
  

  Prompts may be subject to a minimum and maximum number of characters. Even though there are AI interfaces that do not impose a strict limit, extremely long indications can be difficult for AI systems to handle.

• Raise open-ended questions or requests
  

  The purpose of Generative AI is to create. Simple Yes or No questions are limiting and with possible short and uninteresting results. Open-ended questions allow for more flexibility.

• Include context
  

  A generative AI tool can meet a wide range of objectives and expectations, from brief and general summaries to detailed explorations. To take advantage of this versatility, well-designed prompts include context that helps the AI system tailor its output to the intended audience.

• Setting goals or production duration limits
  

  Although generative AI claims to be creative, it is often advisable to include barriers in factors such as output duration. Context elements in prompts may include, for example, requesting a simplified and concise response versus a long and detailed response. Also consider that natural language processing models, such as GPT-3, are trained to predict words based on language patterns, not to count them.

• Avoid contradictory terms
  

  Also derived from long prompts and may include ambiguous or contradictory terms. It is recommended for Prompt engineers to review Prompt training and ensure all terms are consistent. Another recommendation is to use positive language and avoid negative language. The logic is that AI models are trained to perform specific tasks, not to do them.

• Use punctuation to clarify complex cues
  

  Just like humans, AI systems rely on punctuation to help analyze a text. AI prompts can also make use of commas, quotation marks, and line breaks to help the system analyze and operate in a complex query.

Regarding images, it is recommended to consider their description, the environment and mood in their context, colors, light, realism.

How Question Engineering Works

Prompt Engineering is a discipline to promote and optimize the use of language models in AI, through the creation and testing of data inputs, with different sentences to evaluate the answers obtained, based on trial and error until the training of the AI-based system is achieved, following these fundamental tasks:

1. Specify the task: Definition of an objective in the language model, which may involve NLP-related tasks such as complementation, translation, text summary.
2.  Identify inputs and outputs: Definition of the inputs that are required in the language model and the desired outputs or results.
3. Create informative prompts: Creation of prompts that clearly communicate the expected behavior in the model, which must be clear, brief and in accordance with the purpose for which it was created.
4. Interact and evaluate: It is tested using language models and evaluating the results that are returned, looking for flaws and identifying biases to make adjustments that improve their performance.
5. Calibrate and refine: It consists of taking into account the findings obtained, making adjustments until the behavior required in the model is obtained, aligned with the requirements and intentions with which the prompt was created.

Throughout this process, the Prompt Engineer should keep in mind that when designing questions it is critical to be clear and accurate. If the designed message is ambiguous, the model will have difficulties for responding with quality. When designing prompts, attention should be paid to the sources used during the previous training, considering audiences without gender and cultural bias, to promote respect and inclusion. What is recommended is to focus on responses aimed at helping, learning, and providing neutral, fact-based responses

Also, the Role Play application is recommended in which a scenario is created where the model assumes a role and interacts with another entity. For example, if you wish to create a product review, you may take on the role of a customer who tried a product and writes down their satisfactory experience.

The Role of a Question Engineer

A Prompt Engineer es el responsable de diseñar, desarrollar, probar, depurar, mantener y actualizar aplicaciones de IA, en estrecha colaboración con otros desarrolladores de software para garantizar que el software responda y funcione de manera eficiente. En su función se requiere creatividad y atención al detalle para elegir palabras, frases, símbolos y formatos correctos que guíen al modelo IA en la generación de textos relevantes y de alta calidad. Este rol emergente ha cobrado mayor relevancia en la necesidad de que IA contribuya a mejorar y agilizar los servicios ante el cliente y en forma interna. Ahora, si nos preguntamos quiénes puede ser Ingenieros de Preguntas, no solo para agilizar sus tareas sino para desarrollarse profesionalmente, podemos decir que pueden ser los investigadores e ingenieros de IA, los científicos y analistas de datos, los creadores de contenido, ejecutivos de atención al cliente, personal docente, profesionales de negocios, investigadores. Se espera que la demanda de Ingenieros de Preguntas crezca en la medida que las organizaciones requieran de personas que sepan manejar las herramientas impulsadas por IA.

The Future of Prompt Engineering

It is anticipated that trends towards a future of Prompt Engineering will be linked to integration with augmented reality (AR) and virtual reality (VR), in the sense that the proper application of prompts can enhance immersive AR/VR experiences, optimizing AI interactions in 3D environments. Advances in Prompt Engineering allow users to converse with AI characters, request information, and issue natural language commands in simulated, real-time environments. This is based on the fact that, with Prompt Engineering, AI can be provided with a context or situation, a conversation and the exchange of the human being with AR/VR applications, whether for spatial, educational, research or exploration use.

Another of the forecasts of the use of Prompt Engineering is the possibility of achieving a simultaneous translation in spoken and written languages, taking advantage of the contexts in several languages so that AI translates bi-directionally in real time and in the most reliable way possible. The impact of this is communication in business, multicultural, diplomatic and personal contexts, taking into account regional dialects, cultural nuances and speech patterns.

Regarding interdisciplinary creativity, Prompt Engineering can boost AI to generate art, stories, works and music, combining with human creativity. Of course, this may have ethical implications, although the access of AI for artistic purposes is also democratized.

Of course, as Prompt Engineering matures, questions about fairness, respect and alignment with moral values are raised, from the formulation of the query itself to the type of answers that can be derived. Keep in mind that in the future of AI and Prompt Engineering, technology will always be a reflection of people.

Challenges and Opportunities

As we have seen, Prompt Engineering represents the opportunity to develop well-designed Prompts that improve the features of AI, more efficiently and effectively. The advantage of this is that everyday tasks can be streamlined, in addition to expanding knowledge on different topics and boosting creativity. Inclusion is also encouraged when properly implemented, with a positive impact on gender experiences.

On the other hand there are poorly designed questions that can result in AI responses with bias, prejudice, or erroneous data. Hence, ethical considerations in Prompt Engineering can mitigate these risks, without compromising fairness, respect, and inclusion. Also, the lack of application of best practices, even by professionals in the field, may not achieve the desired result on the first attempt and may be difficult to find a suitable point to start the process.

It can also be difficult to control the level of creativity and uniqueness of the result. Often, Prompt Engineering professionals can provide additional information in the message that may confuse the AI model and affect the accuracy of the answer.

Conclusions

In the digital economy, the most memorable experiences will be those in which data is leveraged and combined with human knowledge to anticipate customer needs, with empathy and customization. In this environment, AI becomes the digital partner, not only as a point of contact with the customer, but also as a driver of productivity in the organization. It is true that GPT has gained traction in a search for closer proximity to the customer; however, it is based on frequency and word association, lacking the ability to differentiate correct from incorrect information. Due to this need to improve the quality of answers that Prompt Engineering takes relevance to develop and optimize AI natural language models and obtain quality and accuracy in their answers, based on a greater understanding of user intent. Without a doubt, the demand for the Prompt Engineer will grow, confirming that organizations require professionals who know how to understand the nature of AI-based tools.

It is clear that, as the adoption of Mature Prompt Engineering will continue to raise issues of equity, respect and alignment with moral values in the formulation of prompts and results, so appropriate techniques are required to achieve its implementation without bias or prejudice. To embark on this journey to Prompt Engineering, it is recommended to be accompanied by a technology partner who transmits to their team the best techniques and practices for its implementation.

It would seem like the line between science fiction and reality is increasingly blurred. We no longer see this only in the movies and games, but in e-commerce, education, entertainment, staff training, remote diagnostics or architectural projects. Today Virtual Reality and Augmented Reality are changing the way we use screens by creating new, more interactive and immersive experiences. But… How do we define Virtual Reality and Augmented Reality?

Content:

• Augmented Reality (AR)
• Operation and example of Virtual Reality (VR)
• Similarities and Differences between AR and VR
• Examples of AR and VR Applications
• Using AR on Mobile Devices
• Cinematic Influences on Public Perception of VR and AR
• Technological and Business Challenges
• Future of Augmented and Virtual Reality
• Conclusion

Virtual Reality or VR refers to an artificial environment created with hardware and software, presented to the user in such a way that it looks and feels like a real environment. To “enter” a virtual reality, the user puts on gloves, headphones and special glasses, from which information is received from the computer system. In addition to providing sensory input to the user (three of the five senses: touch, hearing, and vision), the devices also monitor the user’s actions.

For Augmented Reality or AR), technology capable of inserting digital elements into real-world environments is used to offer customers and professionals a hybrid experience of reality. Although Augmented Reality is compatible with multiple devices, it is more popular for smartphone applications, with real-time interactions. In fact, most current AR tools are customer-oriented, although organizations are beginning to embrace AR in business processes, products, and services.

While AR is different from VR, both innovations represent a field of technology called extended reality (XR), encompassing all environments, real and virtual, represented by computer graphics or mobile devices. The goal of XR is to combine physical and virtual realities until users are unable to differentiate them, in addition to being available to anyone to improve their lives.

The importance of understanding each reality (AR and VR) is the potential to alter the digital landscape in life and business, transforming the way we communicate and interact with information and changing the way multiple industries can operate. We look at this in more detail below.

Augmented Reality (AR)

This reality incorporates virtual content into the physical world to improve user perception and interaction with a real environment. AR is experienced with smartphones, tablets or AR glasses, which project virtual objects, text or images so that users can interact simultaneously with virtual and physical elements.

For example, a camera on the device records the user’s environment and gyroscopes and accelerometers monitor the camera’s orientation and location. The AR software analyzes the camera’s transmission, which identifies objects and features in the environment. From there, users may interact with virtual objects using touchscreens, gestures, and voice commands. For example: from a Tablet, you may check suggestions for those who visit a city. The screen shows places to shop, eat, visit a museum, etc., based on the user’s preferences.

On the production floor of a manufacturing company, through AR lenses and software, maintenance engineers can obtain information on the health status of a piece of equipment, so that they can make decisions in real time and more proactively.

Another example can be seen in the design of spaces and architecture. From a lens you may get an image of what the completed project would look like to present the executive project to investors or detect improvements in the design and/or impact on the environment.

Operation and example of Virtual Reality (VR)

Within Extended Reality, virtual reality is the most popular form. Users wear headsets or virtual reality goggles (wearables) that have sensors to track movements and allow them to explore computer-generated virtual environments, as well as to interact with objects and participate in different activities displayed on screens or lenses.

In VR, users see three-dimensional (3D) images that create the feeling of depth and immersion, while spatial audio enhances the experience through headphones or speakers. We must also understand that the immersive experience is a format whose goal is to completely “immerse” the person in a specific real or virtual environment or context.

One of the most widespread applications of VR is in games, in which users interact directly with the game through devices such as glasses, belts, gloves and other accessories that improve player experience.

In industries, VR can support design and training with elements that could be risky for the operator in an induction stage. Risks are also reduced in product design or construction and architecture analysis.

In the field of health, VR has contributed to improvements in the diagnosis of both physical and mental illnesses, training of medical personnel, application of telemedicine, patient education about their condition, or a new approach to recovery or rehabilitation therapies (which transfers the mechanics of games to the educational-professional field). A very important thing in VR is that immersive content is as important as the hardware from which the user has interactions. Without hardware, there is no “simulated environment” that can be brought to life.

To arrive at what we understand today as VR, in 1961, what is considered the first virtual reality helmet was built (by scientists Corneau and Bryan), based on a system of magnetic sensors, incorporating the elements of virtual reality: isolation, freedom of perspective and an immersive sensory experience. A year later, Morton Heilig presented the Sensorama, which reproduced audiovisual content. Users fitted their heads into a device specially designed to live a three-dimensional experience where even odors were reproduced. It was a mechanical device, predating digital electronics. In 1965, Ivan Shuterland, laid the foundations of a multi-sensory system based on a computer. In 1968, Shutherland created the first head-mounted display (HMD) for use in immersive simulations. The graphs that comprised the virtual environment the user was in consisted of simple model rooms rendered from the wireframe algorithm (a visual representation of what users will see and interact with). This device was called The Sword of Damocles (the helmet was so large that it needed to be suspended from the ceiling).

Over time, advances were made, until in the 80s and 90s, the game companies Sega and Nintendo developed virtual reality game systems. In 2009, the Oculus Rift glasses emerged in a project by Palmer Luckey, to develop devices in the video game industry. Until the 2010s, the Oculus and HTC Vive companies introduced high-quality virtual reality headsets, being able to take advantage of powerful graphics and motion tracking technology. Recently, Apple, with Vision Pro, and Meta, with Oculus Go headphones, have generated great expectations about the use of VR.

As for the origins of AR, in 1974, Myron Kruger, a computer scientist and artist, built a lab at the University of Connecticut called ‘Videoplace’ that was entirely dedicated to artificial reality. Within these walls, projection and camera technology were used to cast on-screen silhouettes surrounding users for an interactive experience. Then, AR came out of the labs for use in different industries and commercial applications. In 1990, Tom Caudell, a researcher at Boeing, coined the term “augmented reality”. In 1992, Louis Rosenburg, a researcher at the USAF’s Armstrong’s Research Lab, created “Virtual Fixtures,” which was one of the first fully functional augmented reality systems. The system allowed military personnel to virtually control and guide machinery to perform tasks such as training their U.S. Air Force pilots in safer flying practices.

In 1994, Julie Martin, a writer and producer, brought augmented reality to the entertainment industry with the stage production Dancing in Cyberspace. The show featured acrobats dancing alongside virtual objects projected onto the physical stage.

In 1998, Sportsvision broadcasted the first NFL game live with the 1st & Ten virtual graphics system, also known as the yellow yard marker. The technology showed a yellow line overlaid on top of the transmission so you could quickly see where the team advanced to get a first try. In 1999, NASA created a hybrid synthetic vision system for its X-38 spacecraft. The system leveraged AR technology to help provide better navigation during test flights.

From those years to the present, AR has been widely adopted in various fields such as entertainment, industrial, personnel and design: Esquire magazine used augmented reality (2009) in print media for the first time in an attempt to bring the pages to life. When readers scanned the cover, the augmented reality-equipped magazine showed Robert Downey Jr. talking to readers. Volkswagen introduced the MARTA (Mobile Augmented Reality Technical Assistance) app in 2013, which mainly provided technicians with step-by-step repair instructions within the service manual. Google introduced Google Glass, which is a pair of augmented reality glasses for immersive experiences. Users with AR technology communicated with the Internet through natural language processing commands, being able to access a variety of applications such as Google Maps, Google+, Gmail and others. In 2016, Microsoft introduced HoloLens, which is a headset that runs on Windows 10 and is essentially a wearable computer that allows users to scan their environment and create their own AR experiences. In 2017, IKEA launched its augmented reality app called IKEA Place that changed the retail industry forever. The app allows customers to get a virtual preview of their home décor options before making a purchase.

Similarities and Differences between AR and VR

As we have seen, AR and VR are quite similar and offer virtual objects in real life. Their similarities can be summarized like this:

• They can display enlarged and life-size objects and use the same devices.
• 3D content is needed.
• They can be used on laptops, PCs, smartphones, etc.
• They include tracking of movement of hands, eyes, fingers and more.
• Inmersion is offered.

However, there are differences that we can summarize as follows:

As for the audiences for which each one is focused, AR is for those who need to add virtuality to the real world and improve both the virtual and real world. AR detects user locations and bookmarks, as well as system calls in predefined content. It is partially immersive and open. While VR is for those who need to replace the whole reality and improve virtual reality for many purposes, such as games, marketing, etc. taking into account that VR is an immersive sequence of animations, URLs, videos, audio. VR is fully immersive and closed.

Examples of AR and VR Applications

Some examples of how organizations have adopted AR are:

• Development of translation applications. These applications interpret text, which is scanned, from one language to another.
• In the gaming industry, to develop real-time 3D graphics.
• Analysis and recognition of an item or text. Example: With image capture using Google Lens, the app will start analyzing the image and recognize what it is about. Once done, it will offer you actions to perform related to the type of item or text.
• In advertising and printing, AR is used to display digital content at the top of magazines.
• In design, as we mentioned in the IKEA Place example, AR provides a virtual preview of decoration options before making a purchase. Another example is YouCam Makeup, a free application that allows you to design and create makeup styles, hairstyles, face and body touch-ups with filters, dyes, eyelashes, among others.

VR has gained momentum in several industries, such as:

• The Armed Forces using virtual reality technology to train their soldiers by showing flight and battlefield simulations.
• Medical students learning better with 3D scanning of each organ or the entire body with the help of VR technology.
• Virtual reality being also used to treat post-traumatic stress, phobias, or anxiety by making patients understand the real cause of their illness and in other healthcare settings.
• Professionals using virtual reality to measure an athlete’s performance and analyze techniques with the digital training device.
• Virtual reality-based devices (Oculus Rift, HTC Vive, google cartoon, etc.) helping users imagine an environment that doesn’t exactly exist, such as an immersive experience in the world of dinosaurs.
• From manufacturing and packaging to interior design, companies can use virtual reality to give customers a demo of the product and a better understanding of what goes into making it. An example is Lowe’s Holoroom, where customers can select home décor items, appliances, cabinets, and room designs to see the end result.
• This approach can also be implemented to engage both customers and employees, driving inspiration, collaboration, and interactions. For example, in personal banking, some benefits or rewards can be offered to loyal customers.
• In the specific experience of a particular product, VR makes it possible to highlight its most exclusive features and at the same time provide the opportunity to experience its use. Vehicle manufacturer Volvo used virtual reality technology implementation to help customers who did not have easy access to their dealerships to test their cars. This experience was provided through the use of the Google Cardboard VR headset.

Using AR on Mobile Devices

At first, it seemed that AR would be intended only for military applications or games, but today we see that they play an important role in innovation in the mobile market, allowing users of smartphones and tablets to interact virtually with their environment thanks to greater bandwidth and better connectivity. In the words of Mark Donovan, analyst at ComScore, “…The idea that a mobile device knows where I am and can access, manipulate, and overlay that information on real images that are right in front of me really gets my sci-fi juices flowing…This is just getting started and will probably be one of the most interesting mobile trends in years to come.”.

A major factor in the mobile market is GPS and location-based technologies, which allow users to track and find friends while traveling or “check in” at particular locations. That information is stored and shared with others through the internet cloud and can be used so that marketers can use it to publicize special promotions or discounts, or a city promoting its hotspots could embed facts on the screen and about the neighborhood and the people who lived there. Other visitors may leave virtual comments about the tour. In education, biology students, for example, could use an augmented reality app and a smartphone to gain additional insight into what they see while dissecting a frog.

The way smartphones are driving AR usage, Qualcomm recently showcased augmented reality technology on its devices. Qualcomm’s Snapdragon processors and a new Android smartphone software development kit have been designed to provide the necessary foundation in building and using augmented reality technology in mobile phones. With toy maker Mattel, they collaborated on the virtual update of a classic game called Rock ‘Em Sock ‘Em Robots. Using Qualcomm technology and the smartphone’s built-in camera, players could see virtual robots superimposed on their smartphone screens. The robots appeared in the ring, which was a piece of paper printed with the static image of the ring and its strings. Players used the buttons on their phones to throw punches and their robots moved around the ring while players physically surrounded the table where the image of the ring was placed. The company also sees the potential in marketing, as an example it mentions the insertion of animated coupons on top of real images of its products in stores, so that, when consumers pass by a cereal box, for example, in the supermarket and look at their phone screen, they can get an instant discount.

Now, what is needed for AR on mobile devices? You need a real image capture device, software that is simultaneously transcribing this information and the virtual elements that are going to transform that reality. There are also different types of augmented reality: the one that is transcribed through a geolocation and the one that is based on markers:

• AR Projection: Artificial light is projected onto real-world surfaces. Augmented reality applications can also detect the tactile interaction of this projected light. This way, user interaction is detected by an altered projection on the expected projection.
• Overlay of the AR: Before the overlay, the application must recognize which element it has to replace. Once achieved, an object is partially or totally superimposed.
• AR Markers: Using a camera or a visual marker (a QR, for example), a marker is distinguished from any other real-world object. This way, information is superimposed on the marker.
• Geolocation of AR: It is based on the geolocation emitted by the smartphone through GPS to know its position and location.
• Devices for AR: In AR it is necessary to have sensors and cameras. The projector, usually a very small one, allows you to project reality in any space without using a mobile phone or tablet to interact. Glasses or mirrors also use augmented reality.

There are also 2 types of sensors:

• Sensors used for Tracking: They are responsible for knowing the position of the real world, users and any device in the solution. That way it is possible to achieve that synchronization or registration between the real and virtual world that we discussed when giving the definition of augmented reality. In turn, these sensors are classified into:
  
  • Camera (computer vision): Perhaps one of the most important technologies. There are also the ‘fiducial markers’, that is, marks in the environment that allow the vision system and the solution as a whole, not only to be aware of what is there and what its performance is like, but also to place it spatially.
  • Positioning (GPS): A technology not very specific to augmented reality but also sometimes used for spatial positioning.
  • Gyroscopes, accelerometers, compasses and others: Which allow you to appreciate the orientation (gyroscopes), direction (compasses) and acceleration (accelerometers). Most of these sensors are already incorporated, for example, in mobiles and tablets.
• Sensors to collect information from the environment: Humidity, temperature and other atmospheric information. Another type of possible information is pH, electrical voltage, radio frequency, etc.
• Sensors to collect user input: These are fairly common devices such as buttons, touch screens, keyboards, etc.

Cinematic Influences on Public Perception of VR and AR

Undoubtedly, cinema has been one of the factors that have influenced the perception of Virtual Reality and Augmented Reality. As an example, we have these well-known films where these technologies played a leading role:

• Iron Man: This film is a great example of how military forces can use technology in the field using information fed by a central computer.
• They Live: It is the story of a drifter who discovers a pair of glasses that allow him to see the reality of aliens taking over the Earth. The whole concept of putting on glasses to see what others can’t is the big idea behind AR.
• Minority Report: Futuristic sci-fi film set in 2054, filled with AR technology from start to finish. From the computer interface that appears in the air to the interaction with a 3D computerized board and the ads that offer what the user would like to have.
• Avatar: The main character, Jake Sulley, is on a huge AR device that allows his host to experience a completely different level of sensory perception.
• Robocop: Detroit officer Alex Murphy becomes Robocop. His helmet is connected to the most advanced augmented reality technology that allows him to continue fulfilling his role as a police officer, albeit at a more impressive level.
• Wall-e: futuristic 3D animated film. This film somehow made a subtle statement that AR technology is not just for law enforcement use.
• Top Gun: The HUDs found in the cockpits of the F-14 Tomcats used in the film are the real reason they are called HUDs. These things allowed pilots to keep their heads up in the heat of the action and not look at their instrument panels.
• Tron/Tron: Legacy: Legacy – These two films delve into what could happen if you were unexpectedly thrown into a video game. Although for many passionate gamers it may seem like a dream come true, the movies quickly prove that it is not without its drawbacks.
• Virtuosity: This film poses what could happen if a virtual reality character were placed in our reality. There is a virtual reality simulation built by combining the characters of multiple serial killers that makes its way into reality.
• Matrix: It examines a world dominated by human-created machines, combining action sequences with innovative special effects. Unlike Skynet in the Terminator trilogy, which aimed to annihilate humanity, the artificial intelligence in Matrix has discovered a more useful purpose for our species: energy. Machines do this by absorbing energy from bodies while keeping people entertained in a virtual reality realm known as Matrix.
• Gamer: In the film, users control death row convicts in real life in the Internet Slayers game. Gerard Butler plays one of these convicts and, in order to get released, he must survive the game with the help of the person who controls it. It is an intense and visceral experience that explores the border between virtual and genuine violence.
• Ender’s game: It portrays a society where children are educated to be military soldiers through virtual reality simulations. It’s a depressing concept countered with vivid and extremely beautiful images, particularly in the recreated landscapes.
• Ready Player One: It chronicles how virtual reality has changed cultural conventions thanks to a new technology called Oasis. Although it started as a video game platform, Oasis has expanded into a way of life. People work in the Oasis, children go to school there, and companies try to monetize every square inch of the Oasis. In the game, the winner receives Halliday’s enormous riches, as well as ownership of the Oasis.

In addition to representing the use of AR and VR, the films also raise aspects of ethics and governance as in all emerging technology.

Technological and Business Challenges

AR and VR are technologies that will be increasingly present in people’s daily lives and in the work of companies. Of course, there are challenges that organizations should consider when adopting them:

• Excessive Expectations: It is often speculated that it is possible to execute in virtual environments absolutely all the actions that can be actually performed. It is important to carry out all the necessary procedures so that there is consistency between the virtual and the real world.
• Specific development: Considering that the development of skills in specific fields and in regards to the needs of each organization must be carried out, with defined results from its design in the business model and where a positive impact is generated for the organization.
• Limited resources: Understanding the current limitations in the development of better apps and learning items with AR and VR, from the necessary equipment, software and hardware, and the human talent that can develop and support the applications.
• Technological gap: Reducing the educational-digital gap between institutions, regions and social sectors with access to AR and VR technology and those that do not yet have the same opportunities or technological capabilities.
• Learning Curve: From the first business model where it is planned to integrate AR and VR and the organizational culture that allows the consistent and continuous development of these technologies.
• Transdisciplinary aspects: AR and VR involve transdisciplinary aspects from different knowledge and business areas: information technologies, marketing, sales, operations, human resources, etc.
• Accelerated change: Technology is very agile and the change of electronic devices that give life to this type of tools is updated at an accelerated speed, which triggers challenges in investments in technologies that support it and in the human talent that knows these technologies and that can implement them.

Another important aspect in the reality of many countries is that bandwidth and low latency requirements for these technologies that take up multimedia resources are at an insufficient level, in addition to the fact that current networks often cannot support high-quality AR and VR transmissions, perform high-speed data transmission, stable connection that gets rid of fluctuations and offers a seamless experience.

Future of Augmented and Virtual Reality

While AR and VR remain emerging technologies, faster, lighter and more affordable technology is envisioned in the future. On the one hand, advances in smartphone technology (with better cameras and processors) will mean that you can enjoy more innovative AR and VR experiences. The advancement in 5G wireless networks will also make it possible to enjoy these technologies from anywhere in the world.

Although this high technology is associated with science fiction and the gaming industry, Virtual Reality has the potential to revolutionize several industries, especially when looking for innovative ways to increase their productivity, improve processes and, as remoteness gains ground, the possibilities of virtual reality help achieve goals.

For VR, the development of more powerful processors such as Meta’s Oculus Quest and Apple’s 8KVR/AR headset is anticipated. As devices become more robust in functionality and lighter in use, the adoption of this technology will play an important role in creating more immersive and intuitive experiences in all fields.

We can also mention some predictions and budding improvements:

• LiDAR will bring more realistic AR creations to our phones. iPhone 12 and iPad Pro are now equipped with LiDAR LiDAR (Light Detection and Ranging) technology is essentially used to create a 3D map of the environment, which can seriously improve the AR capabilities of a device. In addition, it provides a sense of depth to AR creations, rather than a flat graphic.
• VR headsets will be smaller, lighter and incorporate more features. Hand detection and eye tracking are two prominent examples of the technology built into virtual reality headsets. Because hand detection allows VR users to control movements without clunky controllers, users can be more expressive in VR and connect with their VR game or experience on a deeper level. And the inclusion of eye-tracking technology allows the system to focus the best resolution and image quality only on the parts of the image the user is looking at (exactly as the human eye does). Delay and risk of nausea are reduced.
• There will be new XR accessories to further deepen the experience. The startup Ekto VR has created robotic boots that provide the sensation of walking, to adapt to the movement in the headphones, even if you are actually standing. The rotating discs at the bottom of the boots move to match the direction of the user’s movements. In the future, accessories like this may be considered a normal part of the virtual reality experience.
• We’ll even have full-body haptic suits. There are already haptic gloves that simulate the sensation of touch through vibrations. The full-body suit is proposed as the TESLASUIT, which today are not affordable for most virtual reality users. Over time they could reduce their cost which in turn will increase their adoption.

According to companies surveyed by PWC in 2022, VR learners absorb knowledge four times faster than learners in the classroom and are 275% more confident when it comes to applying the skills they learned during training in the real world.

In the workplace, remote work is more popular than ever, but there are still aspects of face-to-face interactions that are difficult to replicate. As a result, mixed reality collaborative work tools will increasingly leverage virtual reality and augmented reality to capture and express the more subtle aspects of interaction that are not translated into video calls.

In commerce, virtual reality and augmented reality will more often become part of the marketing and sales process. Brands will invest in creating virtual environments where they can interact with shoppers to help them solve their problems, encouraging them to make the leap from being customers to being loyal followers.

In health, from using AR to improve liver cancer therapy to creating surgery simulations in virtual reality, healthcare systems are using these technologies in a variety of applications. The development continues, due mostly to the growing demand driven by more connectivity, costs in devices that will be reduced and the need to reduce costs and risks in interventions.

According to Forbes, global investments in augmented reality are estimated to grow from $62.75 billion in 2023 to $1,109.71 billion by 2030, at a CAGR of 50.7%. For virtual reality, Forbes estimated that global investments in virtual reality (VR) reached $59.96 billion in 2022 and are expected to grow at a compound annual growth rate (CAGR) of 27.5% from 2023 to 2030. Undoubtedly, double-digit growth makes it clear that organizations must consider how to address these emerging technologies to achieve business results.

Conclusion

AR and VR are technologies that should be reviewed in the Digital Transformation strategy of organizations, for the advantages they represent, from the display for customers of outstanding product characteristics, the feasibility of a project or design; practical guides on the use of products, demonstrations, advertising or promotions; the training and development of staff skills on new equipment or security protocols through VR motivating interactive learning; the holding of virtual meetings or events that simulate the true presence of customers and colleagues; virtual visits to facilities, shops, educational institutions, museums, etc.; up to the best customer service, with a better approach saving time and resources.

Of course, the use of augmented reality and virtual reality depends on the internal capabilities, budget and objectives of the organization. Although there are already many applications on the market that use augmented reality, the technology has not yet become widespread; however, as devices, processors and software add more power and sophistication, the level of information that can be added will increase. AR and VR can help improve decision-making ability, communication, and understanding by experiencing a scenario that is artificial but looks and feels natural. As we have seen throughout this article, AR and VR have many applications in the entertainment, military, engineering, medical, industrial, and other industries. It is recommended, for best results, to combine both technologies by doing an analysis of each use case on adaptability, productivity, time to market, return on investment and expected results. It is also recommended to approach an information technology partner who has the expertise in your industry and understands your challenges.