RAG development: search and answers over your own data
Retrieval-augmented generation built like a product feature — ingestion, retrieval, evals and monitoring — not a notebook demo.
Most RAG projects fail at retrieval, not at the model. The fix is unglamorous: sane chunk boundaries, an embedding model matched to your domain, a reranker on the top candidates, and a labelled evaluation set that tells you whether a change helped.
What the build includes
- Ingestion pipeline: parsing, OCR fallback, deduplication and incremental re-indexing.
- Chunking and embedding strategy chosen against your real documents, not a benchmark.
- Postgres + pgvector storage with row-level security for multi-tenant access.
- Hybrid retrieval (keyword + vector) with a cross-encoder rerank pass.
- Answer layer with enforced citations, refusal path and streaming responses.
- Eval harness scoring recall, faithfulness and latency on every deploy.
Proof it works
DocuMind is my own production RAG product — multi-tenant, running on paid infrastructure, with the architecture written up publicly in the build log so you can judge the engineering before you talk to me.
Frequently asked
What is RAG development?
RAG (retrieval-augmented generation) development is the work of turning your documents into a searchable index, retrieving the right passages at query time, and grounding a language model's answer in those passages with citations. The engineering is in chunking, embedding choice, reranking and evaluation — not in the prompt.
How much does a RAG system cost to build?
A pilot RAG pipeline over one document source is a two to three week fixed-price build. A multi-tenant system with permissions, ingestion queues and evals typically runs six to ten weeks.
Do I need a vector database?
Usually not a separate one. For most workloads Postgres with pgvector is cheaper, simpler to back up and easier to secure with row-level security. A dedicated vector store only earns its place above roughly ten million chunks or with heavy filtered search.
How do you stop a RAG system from hallucinating?
Three layers: retrieval quality measured with a labelled eval set, an answer contract that forces citations to retrieved chunk IDs, and a refusal path when retrieval confidence is low. Every release is scored against the same eval set before it ships.
Related reading
Want a scoped quote? Email daniyal@agenticcore.tech — I reply within one business day.