Context Engineering Foundations / Core Components

Knowledge Base

Beginner [2/5]
Fact repository Information repository

Definition

A knowledge base is a structured repository of domain-specific information that can be retrieved and provided to LLMs as context. It serves as an external memory that extends the model's knowledge beyond its training data.

Knowledge bases are fundamental to RAG systems and enterprise AI applications that need accurate, up-to-date, and domain-specific information.

Key Concepts

  • Document storage: Raw content like PDFs, web pages, documents
  • Vector embeddings: Numerical representations for semantic search
  • Metadata: Information about documents (source, date, category)
  • Indexing: Organizing content for fast retrieval

Examples

Document Types
Knowledge Sources
Knowledge Base Contents: ├── Product Documentation │ ├── user_manual.pdf │ ├── api_reference.md │ └── troubleshooting.html ├── Company Policies │ ├── hr_handbook.pdf │ └── security_policy.md ├── FAQ Database │ └── customer_questions.json └── Release Notes └── changelog.md
Retrieval Flow
Query to Answer
User: "What's the return policy?" 1. Query → Vector embedding 2. Search knowledge base for similar content 3. Retrieve: "Returns accepted within 30 days..." 4. Inject into LLM context 5. LLM generates grounded answer Answer: "Our return policy allows returns within 30 days of purchase with original receipt..."

Interactive Exercise

✎
Design a Knowledge Base

You're building a customer support bot for a software company. What documents would you include in the knowledge base?

Pro Tips
  • Keep content up to date—stale information degrades quality
  • Include metadata for filtering (e.g., product version)
  • Chunk documents appropriately for retrieval
  • Monitor what questions the KB can't answer

Related Terms

RAG Vector Embedding Chunking Semantic Search Context Engineering