LLM Evaluation Framework

Closed-loop evaluation harness running an LLM agent against benchmark problems with deployment, scoring and reporting.

Prompt

Create an LLM evaluation framework diagram for a closed-loop benchmark harness.

Layout:
- Input: benchmark tasks and evaluation rubric.
- Pipeline: prompt builder -> LLM agent -> tool environment -> response collector -> scorer -> report generator.
- Add a deployment gate after scoring with pass / fail decision.
- Show feedback loop from error analysis back to prompt builder and agent configuration.
- Include metric boxes for accuracy, cost, latency, safety, and format compliance.

Style:
- Clean MLOps / evaluation architecture diagram on white background.
- Navy pipeline blocks, teal forward flow, coral failure feedback, amber metric badges.
- Use readable labels and consistent spacing.
- Suitable for AI evaluation papers, internal platform docs, and benchmark reports.

Use in Generator

When to use

For LLM benchmarking, agent evaluation and AIOps-style automated test harnesses.

Variations

With safety guardrails

Add a "Safety Filter" node between Run Episode and Score & Log that screens agent actions for unsafe operations (e.g., destructive shell commands) before they execute. Unsafe actions are logged and the episode is terminated.

Tips

Number the phases — readers expect phase ordering in evaluation harnesses.
Place the benchmark database outside the loop. Mixing it into the loop muddles the figure.
Show a metrics store explicitly — without it the "evaluation" word feels incomplete.

FAQ

Can I depict multi-turn agent runs?

Add a small inner loop on the agent: each turn the agent observes the environment and takes an action; the inner loop closes when the episode terminates.