Building AI Knowledge Bases That Actually Work
Everyone wants faster, smarter AI Agents. But at times, performance problems don't come from the model but from the knowledge base behind it. A good AI knowledge base isn't about storing more documents; it's about making the right information easy to retrieve when it matters most.
What Is a Knowledge Base?
In simple terms, a knowledge base (KB) is a library of information about your team or company's products, services, processes, use cases, etc… That your AI agent searches to answer questions, in short, the agent's reference material.
However, creating an effective knowledge base isn't about dumping PDFs, Excel files, and Word documents into a folder and hoping for the best. It's about how information is structured, indexed, and retrieved. Done well, it improves response quality and reduces latency. Done poorly, it turns even the best AI into a confused intern.
How AI Uses Your Knowledge Base
Modern AI systems use retrieval-augmented generation (RAG). Here's how it works in simple terms:
1. The AI receives a question
2. It searches the knowledge base for relevant chunks
3. It uses those chunks to generate an answer
Step 2 (the retrieval) is where indexing lives and where most problems start.
Is Indexing Important?
Short answer: Yes. Long answer: It depends on how you do it.
Indexing determines how quickly and accurately your AI agent finds relevant information. Remember: more data doesn't always mean better answers. Sometimes it just means slower ones.
Without good indexing:
1. The AI retrieves too much irrelevant content
2. It misses critical context
3. Response time increases due to inefficient searching
With good indexing:
1. Queries return fewer, higher-quality results
2. Latency drops
3. Answers feel more confident and precise
4. Your system scales reliably
One of the most common indexing mistakes is: Indexing Documents As-Is. Large documents (policies, reports, runbooks, SOPs… indexed whole). From an AI perspective, that's like being handed a 200-page book and told: "The answer is somewhere in here." This impacts both accuracy and speed. To reduce waiting time:
1. Keep chunk sizes consistent
2. Avoid indexing redundant content
3. Periodically re-index to remove outdated material
4. Limit how many chunks the AI retrieves per query
My Best Practices
Chunk Documents Intentionally (Not Arbitrarily)
Chunking isn't just splitting text every X characters. Rule of thumb: If a human could answer a question using only that chunk, it's probably a good chunk.
Good chunks:
Contain a single idea or concept
Are understandable on their own
Include just enough context to be useful
Avoid:
Chunks that start mid-sentence
Chunks that depend heavily on previous sections
Massive chunks "for safety" (which defeats the purpose)
Use Metadata Wisely
Metadata is how your AI understands where information comes from. Useful metadata includes:
Document title
Section or heading
Date or version
Source system
Business domain (security, HR, compliance, etc.)
Choose the Right Indexing Method
There are various ways to index documents, but if users ask questions in natural language, semantic indexing is usually most effective.
Start With Document Quality
Before you even think about embeddings or vector databases, go back to fundamentals: the way your documents are written and labelled directly impacts how well an AI can retrieve and use them.
Clear, Descriptive Titles
Vague labels like "General Notes," "Miscellaneous," or "Updated Process" don't help us and won't work for AI agents either. Titles should signal intent and context immediately:
✅ "Incident Response: Initial Triage Checklist"
✅ "Customer Data Access Policy (EU)"
❌ "General Notes"
❌ "Miscellaneous"
Simple rule: If a human wouldn't click on the document, your AI probably won't retrieve it effectively either.
Strategic Tagging
Tags shouldn't mirror folder structures or internal taxonomies. Instead, they should describe meaning:
Domain: cloud, fraud, HR
Function: investigation, monitoring, prevention
Risk area: data leakage, identity abuse, sanctions
Avoid: Team names, internal acronyms no one remembers or knows (orgs change and acronyms with it), and file-location logic. Those may help your storage system, but not the agent retrieval quality.
Mock Example of Indexed Entries
Indexed Entry 1
Chunk Title:
Initial Triage for Suspicious Cloud Storage Access
Chunk Content:
Describes how to validate suspicious cloud storage access by comparing activity against known business workflows, geolocation patterns, API usage, and access timing.
Tags:
cloud security
incident response
data access
threat analysis
Metadata:
Source Document: Cloud Storage Incident Response Guidelines
Section: Initial Triage
Cloud Platform: Multi-cloud
Audience: Security Operations
Version: 2.1
Last Reviewed: 2024-09-18
Indexed Entry 2
Chunk Title:
Escalation and Evidence Preservation for Cloud Storage Incidents
Chunk Content:
Explains when and how to preserve logs, identify impacted storage resources, and escalate potential unauthorized access to cloud investigation teams.
Tags:
cloud investigations
incident escalation
logging
digital forensics
Metadata:
Source Document: Cloud Storage Incident Response Guidelines
Section: Escalation Procedures
Cloud Platform: Multi-cloud
Audience: Security / Cloud Investigations
Version: 2.1
Last Reviewed: 2024-09-18
This index would work because when an AI agent receives a question like: "What should we do when cloud storage is accessed from an unusual location?"
It doesn't need the full document. It retrieves:
Entry 1 for validation steps
Entry 2 if escalation is required
Each chunk:
Answers a specific question
Carries its own context
Includes trust signals (source, version, audience)
This leads to faster retrieval, clearer answers, and fewer hallucinations.
Knowledge Base Maintenance
Knowledge bases are living files: documents get updated, policies change, and old guidance becomes a risk. The best maintenance approach:
Version documents explicitly
Retire old content instead of keeping it "just in case"
Re-index regularly
An AI that relies on a poorly indexed KB will fail so, think like you are the AI agent: You're not actually reading you're searching under pressure. So, your job when building a knowledge base is to make information:
Easy to locate
Easy to understand
Easy to trust
Final Thoughts
If your AI agent feels slow, inconsistent, or vague, don't blame the model first look at the knowledge base. You may be facing an information architecture problem, and that's something entirely within your control. Remember, good indexing:
Reduces response time
Improves answer quality
Makes systems easier to scale and maintain