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.
Chunk on Structure, Not Character Count, So You Retrieve Fewer (and Smaller) Chunks
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More in Retrieval & RAG
🔎Retrieval & RAG
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.
Cache the Context, Not Just the Answer
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.
🔎Retrieval & RAG
70-95% on document-heavy prompts
Stop Pasting Whole Documents: Retrieve the 3 Chunks That Actually Answer the Question
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.
🔎Retrieval & RAG
Often cuts retrieved chunks (and their tokens) by a third or more on filterable corpora; varies by how selective your filters are.
Filter by Metadata Before You Search
Attach structured metadata to chunks and apply WHERE-style filters before the vector search runs, so you embed and rank a smaller candidate set and stuff fewer off-topic chunks into the prompt.