AI & LLMs

Your RAG system returns confident but wrong answers. The LLM is fine in isolation. How do you debug the pipeline?

In retrieval-augmented generation (RAG), answer quality is capped by retrieval quality: if the right chunk never reaches the context window, no prompt can save you. Debug retrieval and generation separately, since they fail for different reasons and usually only one of them is actually broken.

Your LLM feature costs $40k/month and is growing 30% monthly. Leadership wants it cut by 70% without hurting quality. What do you do?

Most LLM spend goes on tokens that never needed generating, requests that never needed a frontier model, and identical prefixes reprocessed on every call. Measure where the money actually goes first, then attack caching, routing and prompt size before anything as drastic as self-hosting.

You have 50M embeddings, need p99 under 50ms, and 95% recall is acceptable. Which vector index do you choose?

Vector index choice is a three-way trade between recall, latency and memory. At tens of millions of vectors with a tight latency budget and tolerance for approximate results, HNSW with quantization is the standard answer: exact search cannot meet the latency, and unquantized HNSW cannot meet the memory budget affordably.