Vector similarity is approximate, so people inflate top-k to avoid missing the answer. A cheap reranking pass lets you fetch many candidates but send only the few that matter to the expensive LLM.
Add a Reranker and a Hard Token Budget: Retrieve 20 Candidates, Send Only the Best 3
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Eliminates the embedding API call and vector search on cache hits. The saved cost tracks your hit rate; it cuts embedding/retrieval spend, not the tokens sent to the LLM.
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Cache the retrieved chunk set keyed by a normalized query, so popular or repeated questions skip the embedding call and vector search and reuse the same context block instead of rebuilding it every time.
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Dumping a full PDF or knowledge base into every prompt bills you for thousands of tokens the model never needed. Retrieve only the passages relevant to the question instead.
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20-40% fewer retrieved tokens at equal accuracy
Chunk on Structure, Not Character Count, So You Retrieve Fewer (and Smaller) Chunks
Naive fixed-length chunking splits ideas mid-sentence, forcing you to retrieve more chunks (and more overlap) to capture one answer. Chunk on semantic boundaries to send fewer tokens per query.