Knowledge Base

A knowledge base is a repository of information that AI systems can access to answer questions accurately. Instead of relying only on what the AI learned during training, it can search your specific documents, data, and content.

Knowledge bases can include:

• Documentation and help articles
• PDFs, Word documents, presentations
• Website content
• FAQ databases
• Product information
• Internal wikis
• Customer support transcripts
• Any text-based information

When integrated with an AI system (via RAG), the knowledge base becomes the AI's reference library—grounding responses in your actual content rather than general knowledge.

Accuracy AI responses are based on your actual documents, not guesses. When the AI says "according to our policy," it really is.

Currency Update your knowledge base, and the AI immediately has access to new information—no retraining required.

Control You decide what information the AI can access. Exclude sensitive data, include approved content.

Transparency AI can cite sources, letting users verify information and building trust.

Specialization Make a general AI an expert in your specific domain by providing your expertise as searchable content.

Compliance Ensure the AI only shares approved, accurate information—critical for regulated industries.

Start with existing content Gather documentation, FAQs, help articles, and common questions your team answers repeatedly.

Organize logically Structure content by topic, product, or user journey. Clear organization improves retrieval.

Write for search Use clear headings, complete sentences, and explicit topic statements. AI search works best with well-structured text.

Cover common questions Analyze support tickets, chat logs, and search queries to identify what people actually ask.

Keep it current Establish processes to update content when products, policies, or information changes.

Remove contradictions Conflicting information confuses both AI and users. Ensure consistency across documents.

Include context Don't assume knowledge. Each piece of content should make sense on its own.

Procedural How-to guides, step-by-step instructions, tutorials "How to reset your password"

Reference Technical specifications, API documentation, product details "Supported file formats: PDF, DOCX, TXT"

Conceptual Explanations of ideas, background information "How our pricing model works"

Policy Rules, guidelines, terms, conditions "Refund policy for digital products"

Troubleshooting Problem-solution pairs, error resolution "Error 404: Check that the URL is correct"

FAQ Direct question-answer pairs "Q: Do you ship internationally? A: Yes, we ship to 50+ countries"

Each type serves different user needs. A comprehensive knowledge base includes multiple types.

Chunk appropriately Break content into retrievable units. A single large document retrieves poorly; many tiny fragments lack context. Aim for 200-500 words per chunk.

Preserve context in chunks Include document titles, section headers, and relevant metadata in each chunk.

Use clear, searchable language Avoid jargon where possible. Write the way users ask questions.

Test with real queries Search your knowledge base with actual user questions. Fix gaps and improve content based on results.

Monitor usage Track which content is retrieved frequently, which queries find nothing, and which retrieved content gets negative feedback.

Version and audit Keep history of changes. Know what content was available when issues occurred.

Quality > quantity A small, accurate knowledge base beats a large, unreliable one. Every piece of content should be something you'd want the AI to quote.