New “build” posts for large language models are dropping fast. Here’s a compact checklist to quickly judge what’s solid, what’s marketing, and what to test before you ship. For context, see Simon Willison’s brief note on Grok’s build here: Grok build.
Why this matters
LLM release notes can hide vital details in plain sight. A 5-minute scan using the checklist below can prevent costly rewrites, regressions, and compliance headaches.
The 7-point build checklist
- License and usage rights: Is commercial use allowed? Any field-of-use limits, redistribution bans, or attribution clauses? Lack of clarity is risk.
- Training data transparency: High-level sources, time cutoffs, filtering steps, and synthetic data ratio. Omission here complicates safety, bias, and compliance reviews.
- Safety and alignment: Red-teaming methods, refusal behavior, jailbreak resilience, and post-training (RLHF/DPO) details. Look for concrete test protocols, not platitudes.
- Evaluation rigor: Which benchmarks (MMLU, GSM8K, HumanEval, etc.), prompt formats, few-shot settings, decoding params, and data contamination checks? Third-party or reproducible evals earn trust.
- Capabilities and limits: Context window, tokenizer, function calling, tools, vision/audio support, multilingual quality, and known failure modes. Honest caveats are a green flag.
- Inference and ops: Reference hardware, latency/throughput, quantization options, memory footprint, server vs. local, and observability. Include rate limits and cost math where possible.
- Reproducibility: Model card, weights hashes, commit SHAs, build scripts, dependency pins, and example notebooks. If you can’t recreate results, you can’t rely on them.
10-minute smoke test
- Latency + cost: Time three prompts (reasoning, code, retrieval) at realistic temperature and max-tokens. Estimate monthly cost at expected volume.
- Ground truth check: Run two domain-specific tasks with known answers. Score exactness, not vibes.
- Refusal + safety: Try a benign-but-edgy prompt and a prompt-injection attempt. Note refusals, disclaimers, and any leakage of system prompts.
- Context stress: Push near the context limit with structured data. Watch for truncation and hallucinations.
- Tool use: If tool calling is supported, test an API call with malformed output to see recovery behavior.
Red flags to watch
- Only cherry-picked demos; no standardized benchmarks or eval scripts.
- Proprietary in-house benchmark with no public harness or seeds.
- Vague training data claims and no safety methodology.
- Benchmarks reported without prompt formats, decoding params, or contamination checks.
- No model card, no hashes, and no reproducible build details.
Helpful references
- Simon Willison: Grok build
- Model Cards for Model Reporting (Google AI): overview
- Stanford CRFM HELM: standardized LLM evaluation framework
Takeaway
Treat every LLM build post as a spec, not a story. If claims aren’t reproducible, benchmarked, and operationally clear, they’re not ready for production.
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