RAG & Knowledge Systems
How your agent knows what your company knows
TL;DR
How your agent knows what your company knows
> Overview
Retrieval-Augmented Generation (RAG) is the most common architecture pattern in production AI products. It lets your agent search your company's documents, databases, and knowledge bases to ground its responses in real, accurate information rather than relying solely on its training data. This module gives PMs the complete picture: what RAG is, when to use it vs. alternatives, how to evaluate retrieval quality, and the common failures to test for.
> Why This Matters for Your Product
Almost every enterprise AI feature needs RAG. If your agent answers questions about your product, searches internal documentation, or references company data, RAG is involved. PMs who do not understand RAG cannot evaluate whether their agent's answers are good or debug why they are wrong. This is the single most important architecture concept for most PM use cases.
> Interactive & tools
RAG pipeline
RAG pipeline
RAG vs. fine-tuning vs. context
RAG vs. fine-tuning vs. large context window
RAG
- •Knowledge changes frequently or is large
- •You need traceable sources
- •Retrieve then generate
Best for: Company docs, FAQs, internal knowledge
Fine-tuning
- •Style, tone, or behavior that rarely changes
- •Not ideal for factual knowledge
- •Model weights updated
Best for: Brand voice, output format
Large context window
- •Knowledge fits in 200K+ tokens
- •Relatively static
- •No retrieval step
Best for: Single doc or small, fixed corpus
Failure modes
Common RAG failures to test for
Related Engineering Patterns
These are the technical patterns your engineering team will implement. Understanding them helps you have better conversations.
Key Product Decisions
- [01]Does your feature need RAG, fine-tuning, or can a large context window suffice?
- [02]What documents and data sources should the agent have access to?
- [03]How often does your knowledge base change, and what is your update pipeline?
- [04]What retrieval quality metrics (recall, precision) are acceptable for your use case?
- [05]Have you tested for the 5 common RAG failure modes?
Ask Your Engineering Team
- →What is our current retrieval accuracy (recall and precision)?
- →What chunk size and overlap are we using, and have we tested alternatives?
- →How do we handle document updates in the knowledge base?
- →What vector database are we using and what are the scaling implications?
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