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LLM Evaluation Strategies

Plan and build tests that measure how well an LLM application performs, using metrics, judges and human review

Source Download skill
Repository
wshobson/agents
Stars
42,095
Licence
MIT
Files
2 files

What it does

This skill teaches an AI agent how to evaluate LLM-based applications systematically. Once installed, the agent can recommend and write evaluation setups that combine automated metrics such as BLEU, ROUGE, BERTScore and accuracy, human rating criteria like factuality and helpfulness, and LLM-as-judge scoring that uses a stronger model to grade a weaker one. It also gives a basic Python pattern for running a set of metrics over test cases and averaging the results.

Great for

  • Developers building chatbots, summarizers or RAG search features who need to check quality before release
  • Teams comparing two models or two prompt versions and wanting a repeatable scorecard
  • Engineers setting up regression tests so a prompt change does not silently break behaviour

Skip it if

  • Non-technical users who only want a one-off quality opinion on a single answer; the skill is oriented to building evaluation pipelines, not casual review
  • Projects where the evaluation data is sensitive: LLM-as-judge setups send test inputs and outputs to a model provider, which may be a privacy or compliance issue
  • Judging truly creative or subjective output with only automated metrics, since BLEU and ROUGE measure word overlap and can mislead

Try asking

  • “Set up an evaluation suite that scores my summarization prompt with ROUGE and a groundedness check before I deploy it”
  • “Compare two of my models on a 50-question test set and report accuracy and BERTScore side by side”
  • “Design an LLM-as-judge rubric for scoring helpfulness and factual accuracy of my support chatbot”
  • “Help me create a human evaluation form with dimensions like coherence, relevance and safety”
Step-by-step guide: how to use LLM Evaluation StrategiesInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 2 original files, the MIT licence and an attribution note.

    llm-evaluation.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip llm-evaluation.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip llm-evaluation.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Give the agent your actual application goal and a few example inputs and expected outputs so it can choose the right metrics rather than listing all of them
  • Ask for a small hand-checked test set first and verify the metrics agree with human judgement before scaling up
  • If you use an LLM judge, ask the agent to also state the judge model, its prompt and how you will check its reliability

What's inside

  • references/details.md
  • SKILL.md

Redistributed unmodified from wshobson/agents under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.