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LLM Eval System Design Playbook

Design a production eval program for your LLM product feature — measurable quality dimensions, a contamination-guarded golden set, calibrated graders, and CI regression gates with drift monitoring — so quality regressions never reach users on vibes. (Evals the feature itself; for benchmarking coding agents on repo tasks, see the Coding Agent Eval Harness Builder Playbook.)

A 4-step agentic workflow pack for coding built to run with ChatGPT, Claude, and Gemini. Open the Markdown files, fill the variables, and paste into your model. Most buyers get a reviewable result in about 15 minutes.

  • Turn a vague feature description into measurable quality dimensions with severity-rated failure modes and honest acceptance thresholds
  • Design a golden set with segment × difficulty coverage and five contamination guards (sealed holdout, canary items, paraphrase rotation)
  • Assign the cheapest trustworthy grader per dimension — programmatic, rubric with 1–5 behavioral anchors, or bias-controlled LLM-judge
  • Calibrate every grader against human labels with explicit agreement targets before it is allowed to block a ship
  • Wire evals into CI with absolute floors, relative-drop rules, a flake budget, and zero-tolerance rules for critical failure modes
  • Catch what golden sets miss with a production drift monitor: sampled scoring, cheap proxies, alert runbooks, and a weekly review ritual
CChatGPTClaudeClaudeGeminiGemini
promptscart.com / prompt-packs / llm-eval-system-design-playbook
Run in
ChatGPT · Claude +1
Your AI model
Step 1
Eval Requirements Mapper
Describe your LLM feature, any bad outputs you have seen, and what failures cost you — get 4–7 quality dimensions, each classified by how it can be graded.
Step 2 · optional
Golden Set Designer
Paste the requirements map and get a sized coverage matrix — segments × difficulty tiers — where every known failure mode is targeted by an adversarial item.
Step 3 · optional
Grader Architect
Feed in your dimensions and golden-set blueprint and get one primary grader per dimension, chosen by the cheapest-trustworthy rule with the rationale stated.
Step 4 · optional
Regression Gate & Drift Monitor Designer
Paste your grader specs and CI context and get gate trigger points, per-dimension absolute floors plus relative-drop rules, and zero-tolerance rules for critical failure modes.
Output
Your deliverable
Copy-paste ready
One-time
$10
~3 hrs / week
time back

Prompt Customization Serviceoptional help adapting variables and output to your brand voice. Choose your tier at checkout (not tied to this prompt's price).

Instant download after payment
Refund as per the Refund Policy.
Email Support · 24h SLA
Lifetime updates

Models supported
C ChatGPTClaude ClaudeGemini Gemini
Best valueSave $786
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Download LLM Eval System Design Playbook

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What ships with your purchase

Prompt files

Plain Markdown files with `{{variables}}` you fill in, ready to paste into ChatGPT, Claude, or Gemini. No setup, no tooling required.

Usage guide

Variable reference, model compatibility, examples, and customization tips so you can adapt the pack to your brand voice.

Lifetime updates

When we improve the pack, you get the new version automatically. Email support included with every purchase.

Models tested: ChatGPT, Claude, Gemini.

The workflow inside this pack

4 composable prompts you run in order — each one picks up where the last left off.

  1. Step 1

    Eval Requirements Mapper

    Describe your LLM feature, any bad outputs you have seen, and what failures cost you — get 4–7 quality dimensions, each classified by how it can be graded.

  2. Step 2 · optional

    Golden Set Designer

    Paste the requirements map and get a sized coverage matrix — segments × difficulty tiers — where every known failure mode is targeted by an adversarial item.

  3. Step 3 · optional

    Grader Architect

    Feed in your dimensions and golden-set blueprint and get one primary grader per dimension, chosen by the cheapest-trustworthy rule with the rationale stated.

  4. Step 4 · optional

    Regression Gate & Drift Monitor Designer

    Paste your grader specs and CI context and get gate trigger points, per-dimension absolute floors plus relative-drop rules, and zero-tolerance rules for critical failure modes.

Perpetual (lifetime) use license

Your one-time purchase includes an ongoing right to use this prompt pack with the AI tools and models you control for your own and your clients' work — not for resale or public redistribution of the files as a product.

We keep the copyright

The prompt files, guides, examples, and bundled assets stay our copyrighted works (or our licensors'). Payment grants the limited license in our Terms only — it does not transfer ownership.

Need help adapting this prompt to your team? Add Prompt Customization Service at checkout.

FAQ

How long does it take to use LLM Eval System Design Playbook?
Most buyers finish in a few minutes: open the prompt file, fill the variables, and paste into your model. The first run is the slowest because you decide variable values; reuse is instant.
What if I get stuck?
Email support@promptscart.com. Free basic support is included with every purchase, and you'll get a reply from our team within 24 hours. If you need help adapting variables or output, we can schedule a call.
Do I need a paid plan with ChatGPT?
The prompt works on free tiers of ChatGPT, Claude, and Gemini. Heavy use can hit free-tier limits; paid plans get longer context and faster responses, but the prompt itself is the value.
Can I customize the prompt?
Yes, completely. You own the prompt files: edit the role framing, add variables, swap output sections, fork it to match your brand voice. Support can help you plan customizations over email.
What if it doesn't work for me?
Refund as per our Refund Policy (https://promptscart.com/refund-policy). Or add Prompt Customization Service at checkout for help adapting variables and output to your workflow.