LLMs · in Open-weight LLM stack

Claude for Open-weight LLM stack

Eval anchor + spot-check in the Open-weight LLM stack stack. Use Claude on a 30 to 50-row eval to confirm your open-weight pick holds quality on your specific task. Re-run quarterly or whenever you swap providers.

Updated 2026-05-05· 1 weeks ago

Meta AI· Default open-weight modelMistral· EU data residency + codeClaude· Eval anchor + spot-check

Where Claude fits in the workflow

Build a 50-row eval with Claude

Use Claude (or GPT) to draft inputs + expected outputs. Hand-edit to remove ambiguity. This eval is the only way to know your open-weight pick is truly good enough.

Prompt · Eval scaffold for an open-weight LLM swap

I'm evaluating whether to use {{Llama 3.3 70B / Mistral Large / etc.}} in production for {{task description}}. Help me build the eval set.

Task:
"""
{{task: input shape, expected output shape, definition of correct}}
"""

Output:
1. **Eval rows** (50) — table of {input, expected output, why this case matters}. Cover the easy cases, the long-tail edge cases, and 5 deliberately adversarial inputs.
2. **Scoring rubric** — exactly how I score actual outputs against expected. Define partial credit if useful.
3. **Pass bar** — what % score against the rubric should I require before swapping production traffic to the open-weight model?
4. **What I should re-run quarterly** — the 5 to 10 most-important rows that catch regression.

Be ruthless about the adversarial cases. The whole point of evals is the model failing on things YOUR users will throw at it.