What is it?

RAG (Retrieval-Augmented Generation) combines vector search with LLM generation. Instead of relying solely on a model’s training data, RAG retrieves relevant documents from your own data store and feeds them as context to the LLM. This grounds responses in your specific domain knowledge.

Why does it matter?

RAG solves the two biggest problems with vanilla LLM deployments: hallucination and stale knowledge. By anchoring generation in retrieved documents, you get factual responses that reflect your current data — without the cost and complexity of fine-tuning.

Trade-offs

Strengths:

  • Dramatically reduces hallucination for domain-specific queries
  • Works with any LLM without model modification
  • Data stays current without retraining
  • Cost-effective compared to fine-tuning

Limitations:

  • Retrieval quality directly limits generation quality (garbage in, garbage out)
  • Chunking strategy is a critical design decision with no universal solution
  • Embedding model choice significantly affects retrieval accuracy
  • Complex queries requiring reasoning across multiple documents remain challenging

Our take

RAG is a solved pattern for most enterprise LLM use cases. The tooling (LlamaIndex, LangChain, custom pipelines) is mature, embedding models are commodity, and vector databases are production-ready. We keep this at Adopt — if you’re building an LLM application that needs domain knowledge, RAG should be your starting architecture.