Embed the query, fetch the top-k similar chunks, paste them into the prompt.
ConceptWhat it is
Naive RAG is the simplest retrieval-augmented generation loop: a user question is embedded, the closest chunks in a vector store are pulled back by similarity, and both are handed to the LLM as context. It exists because models cannot know what happened after training or what lives in a private document store, and retraining for every new fact is wasteful.
It is called "naive" because there is no reranking, no query rewriting, and no correction step — retrieval quality is whatever raw cosine similarity gives you.
How it worksThe mechanics
The pipeline runs in one pass: the document corpus is chunked and embedded ahead of time into a vector index; at query time the user's question is embedded with the same model, the index returns the top-k nearest chunks by cosine or dot-product similarity, and those chunks are concatenated into the prompt before the model generates a grounded answer.
At a glanceSee it
The offline half naive RAG assumes already ran — documents are chunked, embedded, and stored so a live query has something to search against.
The fork that defines naive RAG — when the answer is not in the top-k, nothing reranks or retries, so the model confidently fills the gap.
When to use itWhere it fits
- Quick internal FAQ or docs bot where retrieval quality only needs to be good, not perfect.
- Prototyping to prove the RAG concept before investing in reranking or agentic layers.
- Small, homogeneous corpora where chunks are naturally self-contained, like a single product manual.
- Low-latency budgets where an extra reranking hop is not affordable.
When NOT to use itLimits & anti-patterns
- Large, noisy corpora where raw similarity search surfaces near-duplicate or off-topic chunks, causing hallucination.
- Questions that require combining facts from multiple documents, since single-pass top-k retrieval misses cross-document reasoning.
- Regulated domains where unverified, unreranked context can produce confidently wrong answers.
Trade-offsAdvantages & costs
Advantages
- Simple to build and reason about, often shippable in a day.
- Cheap: one embedding call and one generation call per query.
- Easy to debug because the retrieval step is a single, inspectable function.
- Good enough for narrow, well-curated knowledge bases.
Trade-offs & costs
- Retrieval errors propagate silently into hallucinated answers.
- No correction for ambiguous or multi-part questions.
- Chunk boundaries can split context awkwardly, losing meaning.
- Struggles as corpus size and topic diversity grow.
ExampleIn the real world
A startup builds a Slack bot over its own engineering wiki using pgvector and OpenAI embeddings, answering "how do I rotate the staging API key" by retrieving the top three wiki chunks and passing them straight to GPT.ToolsHow to implement it
- pgvectoradds vector similarity search directly to Postgres, minimal new infra.
- LangChainstandard retriever and prompt-template abstractions for wiring the loop quickly.
- OpenAI text-embedding-3solid general-purpose embedding model for chunk and query vectors.
- LlamaIndexopinionated ingestion and query pipeline for getting naive RAG running fast.
Cost & effortWhat it takes
Low cost and low latency: one embedding call plus one generation call per query, index build is a one-time or incremental batch job, minimal engineering effort to stand up.