RAG Pipelines for AI Agents

Retrieval-Augmented Generation (RAG) grounds AI agent answers in your documents, code, or product data. Use RAG when factual accuracy and citations matter more than pure model parametric knowledge.

Minimal retrieval function

def retrieve(query: str, top_k: int = 5) -> list[str]:
    embeddings = embed(query)
    return vector_store.search(embeddings, k=top_k)

Pipeline stages

Stage	Tasks
Ingest	Chunk documents, embed, index (vector + optional keyword)
Retrieve	Hybrid search, reranking, access control
Generate	Prompt with context; require citations in output

Tuning tips

• Match chunk size to content type (API refs vs prose)
• Refresh indexes on deploy via CI/CD
• Store embeddings on cloud platform managed vector DBs
• Log retrieval IDs for production observability

Related guides

• AI agents overview
• Production AI systems