Loading image: Vector Search and RAG in Practice - Verlin Labs AI guide cover image…Vector Search and RAG in Practice
Retrieval-augmented generation grounds LLM answers in your documents, but quality depends on chunking strategy, embedding choice, hybrid search, and citation discipline. This guide walks engineers through the pipeline decisions that separate demo RAG from systems people trust.
Verified learning path
This guide connects to live Verlin Labs programs - not generic AI content. Apply these frameworks in a cohort with mentor feedback and a capstone demo.
The RAG pipeline end to end
Ingest documents → chunk text → embed chunks → store vectors → on query, embed the question → retrieve top-k similar chunks → optionally rerank → prepend chunks to the prompt → generate an answer with citation instructions.
Each stage is a tuning knob. Weak retrieval cannot be fixed by a better generator - garbage context produces confident wrong answers.
- Chunk size: 300–800 tokens common; overlap 10–20% preserves sentence boundaries.
- Metadata filters (team, date, product) often beat raw similarity alone.
- Always log which chunks were retrieved for debugging user-reported errors.
Chunking and embeddings
Split on structure first - headings, paragraphs, tables - not arbitrary character counts. Tables and code need special handling; naive splitting destroys semantics. Choose embedding models matched to your domain language; multilingual content may need dedicated models.
Refresh embeddings when documents change; stale indexes are a silent source of hallucination-adjacent answers.
Retrieval quality checks
Maintain a benchmark set of questions with expected source documents. Measure recall@k and answer faithfulness separately. Add hybrid search (BM25 + vectors) when users query exact SKUs, error codes, or policy numbers.
Rerankers improve precision on the final context window - worth the latency for high-stakes support or legal use cases.
- If retrieval misses the right doc, tune chunking before rewriting prompts.
- Surface citations in the UI so users can verify claims in one click.
- Set "I do not know" behaviour when similarity scores fall below a threshold.
Key takeaway
Production RAG is an information retrieval problem first. Invest in chunking, hybrid search, evaluation sets, and citations - then tune the generator prompt.
