What RAG solves
RAG helps LLM applications use private or external knowledge instead of relying only on the base model.
Core RAG components
A production RAG system requires more than a prompt.
- Document ingestion
- Chunking strategy
- Embedding generation
- Vector database
- Retriever
- Prompt orchestration
- LLM API
- Response evaluation
Common RAG mistakes
Many RAG systems fail because retrieval quality is poor.
- Bad chunking
- No metadata filtering
- Weak evaluation
- No data refresh workflow
- No access control
- No cost monitoring
Production considerations
Production RAG needs monitoring, security, versioning, cost controls, and user feedback loops. The architecture must be operational, not just experimental.
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FAQ
What is RAG architecture?
RAG architecture combines retrieval systems with LLMs so applications can answer using external knowledge sources.
Does RAG need a vector database?
Most RAG applications use a vector database or vector search layer for semantic retrieval.