Deterministic RAG engineering runbooks, retrieval benchmarks, and failure-mode prevention in pure Python.
Are 40% of your vector search results secretly poisoning your LLM prompts? Here is how top engineering teams catch irrelevant chunks before they burn cash.
You pulled 5 chunks from your knowledge base. 4 are gold, but 1 is an outdated 2021 policy. Guess which one your LLM quotes? Here is how to filter chunks one by one.
When your vector search comes up short, does your LLM politely say 'I don't know' or does it invent convincing fiction? Here is the check that prevents silent guessing.
Complex prompts have multiple angles. When your vector search only finds context for half the question, your users get half-baked answers. Here is the recipe to catch it.
High semantic similarity scores are deceitful: they can be 0.95 while leaving out the pivotal paragraph that changes everything. Here is how to audit retrieval completeness.
Architecture Note
Calling a secondary GPT-4o instance to evaluate every retrieved document adds 2 to 4 seconds of latency and thousands in cloud token bills. LayaEval executes deterministic, sub-10ms checks in local Python, letting you audit 100% of production queries with zero token tax.
01 / Latency
Run evaluation synchronously inside your API gateway without hurting end-user latency.
02 / Pipeline
Include retrieval checks in pytest to catch search quality regressions before code ships.
03 / Budget
Pass/fail verdicts and explainable scores with zero opaque black-box scoring or judge bills.
