The next wave of frontier models is coming fast. In a new post, Simon Willison discusses the road to GPT‑5 and GPT‑6 and what might change for builders and buyers (source). Here’s a pragmatic, vendor-agnostic plan to get ready now.
Why this matters
Capability jumps can break budgets, workflows, and risk controls overnight. Teams that prepare evaluation, governance, and rollout playbooks now will move faster with less drama later.
Your 30-day prep plan
- Map 3–5 high-impact use cases. Prioritize by impact vs. risk. Define “definition of done” and acceptance criteria you can score.
- Build a lightweight evaluation harness. Create 100–300 representative prompts with gold answers. Track accuracy, latency, and cost per task. Consider open benchmarks like Stanford HELM for inspiration.
- Abstract your model layer. Support multiple providers and versions. Pin versions, set fallbacks, and log model, temperature, and system prompts for each call.
- Harden data governance. Add PII detection/redaction in prompts and outputs. Define do-not-log zones and vendor privacy requirements aligned to the NIST AI RMF.
- Control cost from day one. Set per-project budgets, token caps, and alerts. Use prompt caching, response streaming, and smart truncation. Track cost per ticket, report, or lead.
- Ready your RAG/fine-tuning path. Clean and chunk your knowledge base, add metadata, and evaluate retrieval precision/recall. Keep a data diet and change log for future finetunes.
- Red-team and safety-gate. Test jailbreaks, prompt injection, and harmful outputs. Measure refusal rates and hallucination under pressure. Gate risky actions and add human-in-the-loop.
Day‑1 evaluation checklist for GPT‑5/6
- Run your harness unchanged. Compare accuracy, latency, and cost against your current model on the same tasks.
- Validate structured outputs. Check JSON/Schema adherence and function-calling reliability under load.
- Test long-context and multi-step tasks. Measure retrieval fidelity, reasoning depth, and error compounding.
- Probe determinism. Repeat low-temperature runs and quantify variance in critical workflows.
- Assess multilingual and accessibility. Try non‑English inputs, speech-to-text, and screen-reader-friendly outputs.
- Stress throughput. Benchmark concurrency limits and tail latencies during peak usage.
- Quantify total cost per outcome. Report cost per doc summarized, ticket resolved, or lead qualified—not just per 1K tokens.
- Re-run safety tests. Check PII leakage, toxicity, self-harm content, and policy compliance.
Sources and further reading
Simon Willison’s perspective on the road to GPT‑5/6 is a useful frame for practitioners: gpt-5-6. For governance and evals, see NIST AI RMF and Stanford HELM.
Takeaway
Don’t wait for the release note. If you have a use-case map, evaluation harness, and guardrails now, you can adopt GPT‑5/6 on day one—safely and with confidence.
Like this? Get our weekly, ultra-practical AI briefs—no fluff. Subscribe to The AI Nuggets Newsletter.
