The Evolution of Technical Manuals

Technical manuals have undergone a significant transformation since their inception. Once bulky and hard to navigate, they are now digitalized, often in PDF format with advanced search functions and rich visual content. This digital shift has allowed for greater accessibility and ease of use, enabling users to find the information they need more quickly.

Current Challenges of Technical Manuals

Despite these advancements, technical manuals still face several challenges that can reduce their effectiveness:

• Lack of Connected Information – Useful information is often scattered across different documents and not interconnected. This makes it difficult for users to understand the full context and can result in missed or skipped important steps.

• Redundant Content – Manuals often contain repetitive information spread across multiple documents, leading to confusion and inefficiency. Users can easily find themselves unsure which version or piece of information is the most recent or accurate.

• Language and Terminology – Differences in terms between documents or even within the same manual can complicate understanding. A user searching for documentation might encounter different terms for the same component or action, complicating searches and slowing down the process of locating the correct information.

• Time-Consuming Search – Finding the correct information in traditional manuals often requires sifting through multiple sections, chapters, and paragraphs, which can be frustrating and inefficient, especially when a quick answer is essential.

How Can AI Transform the Use of Technical or Any Documentation?

AI has the potential to significantly improve how we use technical manuals by addressing the challenges mentioned above:

• Bridging Language Barriers – A smart manual with an AI component can understand and adapt to variations in terminology. Whether a user refers to a part as a “screw” or “bolt” or uses terms like “set up” or “configure,” the AI component can recognize the context and ensure access to relevant information. This functionality greatly enhances data accessibility and accuracy.

• Interactive Assistance – Imagine a manual that answers you in real-time, asks follow-up questions to clarify your needs, and offers guidance aligned with the context you are discussing. The AI component can enable such dynamic interaction, greatly enhancing the user experience, making information retrieval intuitive and efficient.

The Potential Impact of Smart AI Manuals

A smart AI manual could completely change the way customer support technicians use the documentation available to them, offering several benefits:

• Top-Tier Technical Support for Your Customers – A smart AI manual can act like a top-tier technical expert with photographic memory, instantly finding relevant information from multiple sources and eliminating the need for manual document searches.

• Comprehensive and Accurate Information – For support professionals, the ability to quickly access detailed and accurate information is critical. AI-driven solutions can streamline workflows, reduce errors, and improve overall efficiency by providing precise answers and guidance in real-time.

• Direct Sourcing of Information – A smart AI manual can already show users accurate references and links to relevant chapters or sections of documents, enabling quick verification and further exploration of the content.

image by freepic

Where Can Smart AI Manuals Be Used?

1. Diagnostics, Troubleshooting, and Maintenance – Customer support technicians can quickly search technical instructions to resolve issues, reducing the time needed to fix reported problems and increasing operational efficiency.
2. Enhanced Customer Support – A smart AI manual allows for faster and more accurate responses to customer inquiries, improving the overall customer experience.
3. Faster Training and Onboarding – New employees can more easily access the information they need, shortening the time required for training and onboarding.
4. Improved Compliance and Security – A smart AI manual could provide IT professionals with up-to-date security protocols and procedures, reducing the risk of non-compliance and improving overall security.

Use AI to Support Your Tasks, But Don’t Let It Take Over

While tools utilizing AI components are already incredibly useful, it’s crucial to understand their limitations and ensure the necessary human oversight:

1. Data Governance is Key – If you don’t know where your relevant documents and data are located or if you lack clearly defined data governance processes, smart AI manuals will be of limited help. As the old saying goes, “Garbage in, garbage out.” Without well-organized document repositories, manuals, and clearly defined internal guidelines, any tools—whether powered by AI or not—won’t be effective.
2. Validation of Initial AI Responses – The initial answers generated by AI need human verification and confirmation. This won’t happen automatically. It’s crucial that subject matter experts (SMEs) review and confirm initial outputs to ensure that future answers are accurate and reliable.
3. Detailed Questions Lead to Better Answers – The more precise your question is, the more accurate the AI answer will be. Like with young children or teenagers, if you don’t provide precise context or the necessary details, you can’t expect perfect answers immediately.
4. AI Doesn’t Replace Human Expertise – I was recently asked whether an AI tool could review all our documentation, create a business glossary, and list key performance indicators (KPIs). While AI tools can assist in locating and organizing data (if the data is properly organized according to your data governance procedures), they do not replace the need for human expertise. If you use an AI tool to assist in creating a business glossary or KPI list, remember that the responsibility for the final versions of those documents and their approval lies with you, the expert in the relevant field. Use AI tools as support in your work, but don’t delegate your responsibilities to them.

AI-driven tools can be powerful assistants in optimizing technical support and navigating various forms of documentation, but they should be used as auxiliary assistants, not as complete substitutes for human knowledge and expertise.

By: Josip Kustić

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In October, we organized our first independent conference in the UK. The impressions finally settled. Without exaggeration and false modesty, we can announce that genAI for BUSINESS was a complete hit.

The conference was held in the academic environment of the Sci-Tech Daresbury campus, which produced not one, not two, but even three Nobel laureates! As if that wasn’t impressive enough, Alan Turing (yes, that Alan Turing) postulated a part of the foundation of modern information science right there in the 50s. To this day, SciTech remains synonymous with scientific excellence, mostly in AI development and nuclear and space research. One of the more memorable landmarks in the county is the characteristic bell-shaped chimneys, like the one you might remember from the Simpsons intro. If you need to know more about the significance of the Sci-Tech campus, an IBM supercomputer and a vast particle accelerator are also their most prolific tenants.

Now that we are spatially and contextually situated, we can turn our heads towards our conference. 

Under the baton of Ina Bičanić Benković, Justin Barnes and Neil Jenkinson masterfully led behind and in front of the scenes. They efficiently managed to seduce the speakers and audience equally with their charm, not to mention the amount of preparation our English lads put into the job.

As for the content, the story was very rounded. During this one-day meeting, the lecturers presented the entire life cycle of the development of LLM projects to the audience. They answered questions like Under what conditions such projects are created, what technologies to use and how to use them, whom are they intended for, what challenges each designer encounters along the way, why the ethical moment is an essential element of every single AI project, and, most importantly, how to raise money for development of such goals. Previous AI projects of the TIS Grupa served as a perfect analysis starting point for each of the questions mentioned above.

From the home team, in addition to the already mentioned, our colorful heptagons were defended by Dženan Lojo and, unexpectedly, Karlo Franić, who ran into the pitch in the last seconds. Due to unexpected complications, he replaced his boss, Tomislav Strgar, at the last minute. We are glad to say Karlo quickly turned into one of the shining stars of the whole event. Once more, it has been proven that a good plan B is the best friend of all challengers of fate.

We want to thank all the speakers and the guests. According to our field agents, the subject was coming up roses. If it’s not apparent, we don’t even try to hide our satisfaction with a well-done job. Still, the most significant confirmation of our success is that our hosts expressed their wish to turn this event into a regular platform for exchanging information on AI.

Learned and inspired by this experience, we throw ourselves into the organization of new forums of this type. .debug, we are coming for you. 

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