RAG combines the power of large language models with your organization's knowledge. Instead of relying solely on what the model learned during training, RAG retrieves relevant context at query time.
The RAG Pipeline
Query → Embed → Search → Retrieve → Augment Prompt → Generate
Chunking Strategies
How you split documents matters:
- Fixed size - Simple but may break context
- Semantic - Split on meaning boundaries
- Hierarchical - Parent-child chunks for context
Vector Databases
Store embeddings for fast similarity search:
- Azure AI Search
- Pinecone
- Weaviate
- Chroma
Prompt Engineering for RAG
Given the following context, answer the question.
If the answer isn't in the context, say "I don't know."
Context:
{retrieved_documents}
Question: {user_query}
Evaluation
Measure retrieval quality and generation accuracy separately. Poor retrieval means irrelevant context. Poor generation means misusing good context.