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Why does RAG cost more than a plain LLM call?

Because a RAG request stuffs retrieved chunks into the prompt, and those chunks are billed as input tokens on the generation call. A 40 token question can become a 3,000 token prompt, so the same question costs several times more than a plain call. The retrieved context is usually the single largest line item, larger than the output, the query embedding, or the vector search.

What is a semantic cache in RAG?

A semantic cache stores past queries as embeddings paired with their answers, and when a new query is similar enough to a stored one, it returns the stored answer and skips retrieval and generation. Unlike an exact-string cache it matches paraphrases, since it compares query embeddings by similarity rather than matching text. A hit costs only one embedding, which is why the saving scales with the hit rate.

How do you set the similarity threshold for a semantic cache?

Start tight, around 0.95, so only near-identical queries hit and almost every served answer is correct, then loosen it while watching the false-hit rate on real traffic. The threshold is a precision versus recall trade: loosening it raises the hit rate and the saving but also serves answers that belong to a different question. A false hit is a confidently wrong answer with no retrieval to catch it, so the threshold is a correctness decision, not just a cost one.

What is incremental re-indexing and why does it save money?

Incremental re-indexing embeds only the chunks whose content changed, plus new chunks, instead of re-embedding the whole corpus on every update. You hash each chunk and compare it to what is already indexed, so unchanged chunks keep their vectors for free. This makes the embedding bill scale with the change rate rather than the corpus size, which matters most for large corpora or expensive embedding models. Switching embedding models is the exception, since it invalidates every vector and forces a full re-embed.

How can I reduce the context token cost in RAG?

Retrieve a wide candidate set, then rerank it down to a few chunks and stuff only those into the prompt. The vector search is cheap so a wide net protects recall, and the reranker keeps the answer-bearing chunk while dropping the noise, so the generation input tokens fall by a large factor without losing the answer. Because retrieved context is the biggest cost bucket, cutting it is usually the highest-leverage cost lever in a RAG system.

When should you not use a semantic cache?

Avoid or heavily restrict it when answers are personalized, time-sensitive, or traffic is mostly novel. Personalized answers depend on who is asking, which a query embedding does not capture, so caching on the query alone serves one user's answer to another. Time-sensitive answers go stale, so they need a short time-to-live, and low-repeat traffic gives a near-zero hit rate that does not justify the overhead. In all cases the cache must be invalidated when the corpus changes, or it serves answers generated from documents you already updated.

You add RAG to a chatbot and the per-answer cost roughly triples, with no change to the model or the user's question. What is the dominant cause, and where does the first cost lever point?

The retrieved context stuffed into the prompt as input tokens; the first lever is cutting context, for example reranking a wide retrieval down to a few chunks, Retrieved context turns a short question into a multi-thousand-token prompt, and those chunks are billed as input tokens on the generation call, which makes context the largest bucket. The query embedding is a fraction of a cent and the vector search is monthly infrastructure. So the highest-leverage move is to send fewer context tokens: retrieve wide to protect recall, rerank down, and stuff only the best few chunks.

A semantic cache is serving answers, and you lower the similarity threshold from 0.95 to 0.85 to raise the hit rate. What is the specific danger you have introduced?

False hits: queries that only look similar get served a cached answer meant for a different question, a confidently wrong answer with no retrieval to catch it, The threshold is a precision versus recall dial. Loosening it raises the hit rate and the saving, but it also lets through queries that sit close in embedding space yet need different answers, such as the refund window for digital versus physical goods. Those false hits are wrong answers served with confidence, and because a hit skips retrieval and generation, no downstream step exists to catch them. That is why you start tight and monitor the false-hit rate as you loosen.

Your corpus of 500,000 chunks is edited by about 2 percent per day. Your pipeline re-embeds the entire corpus every night. What is the cheaper design, and what is the one case where it does not help?

Incremental re-indexing: hash each chunk and embed only changed and new chunks, so cost scales with the 2 percent change rate; it does not help when you switch embedding models, which invalidates every vector, Incremental re-indexing compares a content hash per chunk against what is already indexed, reuses unchanged vectors for free, and embeds only the changed and new chunks, so the embedding bill tracks the change rate instead of the corpus size. The exception is a change of embedding model: the new model produces vectors in a different space, so every stored vector is invalid and a full re-embed is unavoidable, which is why a model swap is a budgeted migration, not a routine update.

For which workload is a query-keyed semantic cache most likely to serve a wrong answer, even with a well-tuned threshold?

A support bot where the correct answer depends on the specific user's account and entitlements, When the right answer depends on who is asking, the query text alone does not capture it, so two users can send the identical question and need different answers. A cache keyed only on the query embedding will match them and serve one user's answer to another, no matter how tight the threshold, because the threshold measures query similarity, not user context. Such answers must be keyed on the user plus the query or not cached at all. Stable, shared-answer workloads like documentation and FAQs are where a query-keyed cache is safe and effective.

RAG Caching and Cost: Semantic Cache, Embedding Reuse, and the Bill

Name: System Design Masterclass
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in Retrieval Rag

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