What Is RAG? Retrieval-Augmented Generation Explained
RAG (Retrieval-Augmented Generation) is a technique that lets AI models access external knowledge bases to generate more accurate, context-aware responses. Instead of relying solely on what the model learned during training, RAG systems fetch relevant information in real-time.
How RAG Works
- Chunk & Embed — Your documents get split into smaller chunks and converted into vector embeddings
- Store — These embeddings live in a vector database (Pinecone, Weaviate, Chroma, etc.)
- Query — When a user asks something, the system searches for the most relevant chunks
- Augment — Those chunks get injected into the model’s prompt as context
- Generate — The model produces an answer grounded in your actual data
Why RAG Matters
- Up-to-date knowledge — Models can answer about information that wasn’t in their training data
- Citation capability — Users can verify where answers came from
- Reduced hallucinations — Responses stay grounded in real documents
- Private data access — Query your internal docs without exposing them to the model
Evaluating RAG Systems
When benchmarking RAG implementations, we track:
| Metric | What It Measures |
|---|---|
| Retrieval precision | Are relevant docs actually being fetched? |
| Context relevance | Does the retrieved info actually answer the query? |
| Answer accuracy | Does the final response use the context correctly? |
| Latency | How long does the full retrieval + generation take? |
| Token efficiency | Are we sending only what’s needed? |
Common RAG Patterns
- Naive RAG — Simple retrieve → generate pipeline
- HyDE (Hypothetical Document Embeddings) — Generate a hypothetical answer, then retrieve similar docs
- Corrective RAG — Evaluate retrieved docs, re-query if needed
- Agentic RAG — Multi-step reasoning with tool use
RAG in Production
The gap between demo and production RAG is significant. Key challenges:
- Data freshness — How often does your knowledge base update?
- Embedding drift — Do embeddings still match your current content?
- Citation accuracy — Does the model actually cite the right sources?
- Cost control — Retrieval API calls add up quickly
We evaluate RAG systems weekly against real engineering documentation, measuring both retrieval quality and end-to-end answer accuracy.