Apr 1, 2026 · Written by: Netspare Team

RAG, Embeddings, and Vector Search: Concepts Developers Should Understand

Retrieval-augmented generation feeds an LLM with retrieved context chunks so answers cite your documents instead of hallucinating freely. Embeddings turn text into vectors for similarity search.

Quality depends on chunk boundaries, metadata filters, and evaluation—not only embedding model marketing scores.

Embeddings and vector indexes

An embedding model maps variable-length text to a fixed-dimension vector; cosine similarity ranks nearest neighbors in the index.

Re-embedding everything on model upgrades is costly—version your embedding model name in metadata to mix safely during migration.

Chunking strategies

Fixed token windows are simple but split tables and code mid-function; structure-aware chunking (per heading, per file) improves coherence.

Overlap between chunks reduces boundary misses at the cost of storage and duplicate hits.

Retrieval + rerank

Hybrid search (BM25 + vectors) helps proper nouns and SKUs embeddings misspell.
Cross-encoder rerankers are slower but improve top-k precision before sending context to the LLM.
Cap total tokens sent to the model; irrelevant chunks add noise and cost.

Evaluation loop

Curate a labeled set of questions with expected source documents; measure recall@k before tuning prompts.

Log retrieval IDs in production to debug “wrong answer” reports quickly.

Frequently asked questions

Do I need a vector database?

For small corpora, pgvector inside PostgreSQL may suffice; dedicated vector DBs help at large scale and filtering complexity.

RAG replaces fine-tuning?

They solve different problems—RAG grounds answers in fresh docs; fine-tuning changes model behavior and style.

Netspare Team