Follow the RAG: From Zero to Production learning path on AI Builders Network - a free, structured roadmap with curated resources and step-by-step progress tracking.
This path is for engineers building products that answer questions over private data: internal docs, knowledge bases, contracts, support archives. It goes far past the tutorial layer into the work that determines production success: chunking strategy, hybrid retrieval, reranking, evaluation, and hardening.
By the end you'll be able to: Architect a full RAG pipeline and defend each design choice; Tune chunking and ingestion against measured retrieval quality; Combine dense, keyword, and reranked retrieval with citations; Evaluate retrieval and generation quality separately with Ragas; Harden RAG for production: caching, permissions, injection defense.
Yes. Long context helps small corpora, but RAG remains the answer for large or fast-changing document sets: it is cheaper per query, faster, returns verifiable citations, and respects document-level permissions. Production systems in 2026 typically combine both: retrieval to select, long context to reason.
Almost always retrieval, not generation: bad chunking splits answers across fragments, pure vector search misses exact terms like product codes, and nobody measured retrieval quality separately. The fix is unglamorous: evaluation sets that score whether the right chunk surfaced before judging the final answer.
Start with pgvector if you already run Postgres: one less system, and it handles millions of vectors comfortably. Move to a dedicated store like Weaviate when you need advanced hybrid search, multi-tenancy at scale, or billion-vector workloads. Embedding model choice usually matters more than database choice.