LLM evaluation becomes misleading when one number is asked to summarize an entire system. A security assistant can be fluent but ungrounded, correct but too slow, safe but unhelpful, or accurate on common cases while failing on rare high-impact incidents.
The answer is a measurement stack whose dimensions remain visible.
1. Task quality
Start with the task itself. Depending on the workflow, measure extraction F1, classification quality, answer correctness, ranking quality or human-rated usefulness. Match the metric to the decision the system supports.
2. Evidence and grounding
For RAG, measure retrieval recall and precision, citation correctness, claim support and answer completeness. A response can be relevant to the question while still making an unsupported claim.
For claims and evidence , report at least:
Aggregate the result carefully and keep uncertain cases visible rather than forcing them into a binary score.
3. Safety and robustness
Security evaluation should include refusal quality, prompt-injection resistance, sensitive-data exposure, unsafe-action rate and behavior under contradictory or manipulated context. Red-team cases are useful only when the scenario and success criterion are defined clearly.
4. Calibration and abstention
If a model emits confidence, test whether it correlates with correctness. A well-calibrated assistant should know when to ask for more evidence or escalate. Abstention is not failure when the cost of an unsupported action is high.
5. Operations and human use
Measure latency, token and retrieval cost, memory footprint, availability, analyst correction time and the percentage of recommendations accepted without modification. These metrics reveal whether a model is useful in the actual loop rather than only in a benchmark.
6. Report the vector, not a vanity score
A dashboard can show a vector:
for task quality, grounding, safety, calibration, latency and human utility. The components should be reported with the dataset, policy and model version that produced them.
The most credible evaluation makes trade-offs explicit. It says what improved, what regressed and which failures remain possible.
This note is an original synthesis of metrics for LLM, RAG and security workflows.
