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Why use component evals instead of one end-to-end score for RAG?

Because an end-to-end score detects that quality dropped but cannot say which layer caused it. Component evals score retrieval, generation, tool calls, and task separately, so a regression comes with a location: the layer whose number moved. You keep the end-to-end score as the true measure of user outcome and add the component scores to localize failures fast.

What is the difference between recall@k and context precision?

Recall@k measures whether the relevant chunks were retrieved at all: the fraction of all truly relevant chunks that landed in the top-k, which sets a ceiling generation cannot break. Context precision measures whether the retrieved relevant chunks are ranked high with little noise around them. They trade off through k, since raising k lifts recall but usually lowers precision, so you report both.

What is faithfulness in RAG evaluation?

Faithfulness is whether every claim in the answer is supported by the retrieved context, computed as supported claims divided by total claims. It is checked one claim at a time, and a claim that is true in the world but not entailed by the retrieved context still counts as unfaithful, because a RAG answer is only allowed to assert what its sources support.

Why is a correct answer from an agent sometimes still a bug?

Because the path the agent took can be broken even when the final answer is right. An agent that ignored a tool error, looped wastefully, or made an unauthorized read might reach the correct answer by luck, and that path fails on the next input where luck does not save it. Trajectory evaluation checks the sequence of steps, not just the outcome, which is why tau-bench's pass^k drops sharply as k grows.

How do you evaluate an agent's tool calls?

Split the tool call into three independent checks: was the right tool selected, were the arguments valid against the schema and semantically correct, and was a tool error handled by recovering rather than pressing on. Argument schema validity can be checked in code with no LLM judge, which is the cheapest reliable agent signal, and error handling is tested by injecting failures like a forced 404 or timeout.

Why decompose a quality grade into binary yes-or-no facets?

Because a binary question has much higher inter-rater agreement than a 1-to-5 score, since a checkable yes-or-no leaves little room for taste while two people rarely share the same line between a 3 and a 4. Higher agreement raises the ceiling a judge can be validated against, and DoorDash used exactly this decomposition, scoring five separate aspects, to help cut hallucinations by about 90 percent.

A RAG system's end-to-end task success drops from 78 to 71 percent. Retrieval recall@5 and faithfulness are unchanged, but tool argument validity fell from 0.94 to 0.71. What does this tell you?

The regression is in the tool layer, so you investigate the tool call arguments and skip retrieval and generation, This is exactly what component evals buy you. The end-to-end score detected the regression, and the per-layer scores localized it: retrieval and generation held while tool argument validity collapsed, so the bug is in the tool layer. You go straight there instead of searching all four layers by hand. An end-to-end score alone would have shown the seven-point drop with no address.

A user's question has its answer only in one chunk, and that chunk was not in the retriever's top-k. Which is true?

Retrieval has capped the outcome; the generator can only guess or refuse, so the fix is upstream in retrieval, Retrieval sets a hard ceiling. If the answer-bearing chunk was never retrieved, the generator is working from context that does not contain the answer, and no prompt, temperature, or model change can conjure a fact it never saw. This is a retrieval failure measured by recall@k, not a generation failure. Blaming generation here is the most common and most expensive misdiagnosis in RAG.

An agent returns the correct final answer, but the trace shows it called a paid API 40 times, ignored two errors, and got lucky. An outcome-only eval marks it pass. What is the right read?

It is a latent bug: the path is broken and will fail on inputs where luck does not save it, which trajectory evals and pass^k expose, A correct answer reached through a wrong path is still a bug, because the path is what runs in production and what breaks when the next input does not get lucky. This is why trajectory evaluation checks the sequence of steps, not just the outcome, and why tau-bench's pass^k metric drops sharply with k: a run that succeeds by luck does not survive being repeated.

Why decompose a single 1-to-5 quality grade into several binary yes-or-no facets, each judged separately?

Binary questions have higher inter-rater agreement than a 1-to-5 score, which raises the ceiling a judge can be validated against and localizes which property is weak, A checkable yes-or-no leaves little room for taste, so two raters agree far more than they do on a 1-to-5 score where the line between a 3 and a 4 is undefined. Higher agreement raises the human ceiling a judge is validated against, and scoring each facet separately shows which property is weak. DoorDash used exactly this decomposition into separately scored aspects as part of cutting hallucinations by roughly 90 percent.

Evaluating RAG and Agents: One Score Detects, Components Localize

Name: System Design Masterclass
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