Alternatives to RAG, explained

What “RAG” usually means

In practice, a RAG pipeline embeds your query with a neural model, searches a vector database for approximate nearest neighbors, often reranks the candidates, and feeds the result to an LLM. It works — but it’s a stack of moving, probabilistic parts.

Where RAG struggles

• Drift — embeddings and approximate search are probabilistic, so rankings can shift between runs and model versions (the hot state).
• Cost — every query pays to embed, search, and rerank; the bill scales with traffic.
• Opacity — a cosine distance isn’t an explanation, so debugging a bad answer is guesswork.
• The gap — when nothing relevant is retrieved, the model fills the gap with a hallucination.

The alternatives

• Keyword / BM25 — fast, cheap, deterministic, but matches words rather than meaning.
• Hybrid — blends keyword and vector scores; better recall, but keeps the embedding cost and some drift.
• Deterministic retrieval — scores relevance without a query-time embedding step, so results are reproducible and explainable, with no per-query inference.

When deterministic fits

A deterministic approach is the strongest fit when you need reproducibility (stable evals, replayable failures), auditability (a reason for every ranking, and citations an agent can verify), or flat cost at scale. If you need the generative creativity of an LLM over fuzzy, in-house documents, classic RAG still has its place — the two aren’t mutually exclusive.