Make your knowledge base AI-ready in 7 steps (Part 1)

Consider this: Large enterprises lose up to 3.6 hours of their workday searching for information Reunionsandnewbestfriends:ThesecrettoSlotomania’slongevityReunions and new best friends: The secret to Slotomania’s longevityReunionsandnewbestfriends:ThesecrettoSlotomania’slongevity, yet many rush to implement AI Assistant solutions without addressing their underlying knowledge structure. The reality is stark: your knowledge base must be structured in a robust and coherent way, not just for your users, but even more critically for the AI agents you plan to deploy.

The success of your AI initiatives depends heavily on the quality of your search experience. If your users struggle to find information through traditional means, AI agents won't magically solve the problem—they'll likely amplify existing gaps and inconsistencies. It's like building a smart home on a shaky foundation; no amount of automation can compensate for structural flaws.

In this guide, we share a proven workflow to transform your document collections into an AI-ready knowledge base, enabling powerful Retrieval-Augmented Generation (RAG) systems. Our approach combines time-tested information retrieval principles with modern AI capabilities, ensuring your knowledge base serves both human users and AI agents effectively.

Before diving into advanced tools and frameworks, it’s essential to understand your data landscape and its domain-specific structures. This foundational step informs all subsequent technical decisions and ensures your knowledge base design aligns with your content’s unique characteristics.

Practical Steps

1. Data Inventory
   • Begin your journey with a comprehensive catalogue of your data landscape.
   • Take inventory of all available sources, from technical documentation and internal wikis to public articles and video transcripts.
   • Identify your most critical sources and prioritise them for processing.
2. Document Analysis
   • Examine creation dates, modification history, authorship, and hierarchical organisation.
   • Identify cross-referencing links, images, and attachments.
   • Understand the interconnected nature of your knowledge base to preserve these relationships properly.
3. Content Chunking Strategy
   • Decide how to break content into retrievable chunks (e.g., paragraph-level chunks).
   • Balance granularity with context preservation.
   • Consider creating a histogram of document sizes in tokens to identify natural breakpoints.
4. Domain-Specific Structures
   • Some content (code snippets, ticket references, diagrams) needs special handling for formatting and functionality.
   • Preserve formatting for specialised elements.
5. Schema Definition
   • Define how documents will be titled, summarised, and categorised.
   • Choose a database (search engine, relational, or graph) that aligns with these schema and retrieval requirements.

Quick tip: Extract and analyse document headers and section names to understand content complexity and common organisational pattern.

It’s always important to review your assumptions of user needs and behavior. That’s why the user research phase is crucial: it reveals the gap between how you think users search for information and how they actually do it. A well-structured knowledge base that doesn’t align with user behavior is destined to fail, no matter how technically sophisticated it might be. Gathering firsthand insights from diverse user personas reveals how people actually find and use information, double-checking the exact users’ pain points and preferences. By focusing on real-world tasks and feedback, you can tailor the knowledge base to better address users’ workflow challenges, improving adoption and overall satisfaction.

Practical Steps

1. The Value of Direct User Research
   • Review existing requirement analyses and past user research.
   • Conduct fresh interviews if data is outdated, targeting how and why users search for information.
2. Persona Identification
   • Identify distinct user types (developers, support engineers, product managers, etc.).
   • Recognise each group’s specific pain points and preferred search patterns.
3. Task Analysis
   • Observe how users navigate documentation or internal wikis in real scenarios.
   • Note any shortcuts, repeated queries, or pain points.
4. Metrics
   • Establish a baseline for search success rates and time-to-find metrics.
5. Interviews
   • Conduct semi-structured interviews for real stories about user frustrations and successes.
   • Example: “How do you troubleshoot an error today?” or “What do you expect from an AI assistant in this context?”
6. Surveys and Usability Studies
   • Validate interview findings at scale using quick online surveys (Google Forms, TypeForm).
   • Gather feedback on features like personalised search, recommended reading, or chat-based Q&A.

Quick Tip: Shadow users during actual troubleshooting scenarios. Observing real behaviors often differs from what they report in interviews.

The first two steps—exploring and structuring your data (Step 1), and deeply understanding your users’ needs (Step 2)—lay the essential groundwork for any AI-powered knowledge initiative. By taking a comprehensive inventory of your content and aligning it with real user behaviour, you create a solid framework that both minimises bottlenecks and drives meaningful engagement. This initial foundation is crucial because it ensures that all subsequent enhancements are built upon clear, coherent, and user-centric information structures.

In our next piece, we’ll focus on metadata enrichment, chunking & indexing (Step 3) and domain expert feedback with iterative quality checks (Step 4). We’ll show you how to programmatically extract and optimise the metadata that fuels precise retrieval, as well as how to involve subject matter experts in refining your knowledge base.