Production Playbook and Model Updates

The eval passes on the bench - beyond that begins life in production and the changing of models. A prompt doesn't end after a successful demo: the production cycle includes versioning, traces, evals, canary, budgets, rollback, and a re-audit after every significant model change.

It helps to keep a checklist by stage - what is ready for each step.

• Before the prompt - the use case, a baseline, success criteria, and the cost of an error.
• Before staging - schema, dataset, tool contracts, and permissions.
• Before production - a release gate, traces, budgets, and rollback.
• After launch - canary, incident review, a drift review, and the cost of one successful result.

Migrating to a stronger model isn't a reason to rewrite everything. Fix a baseline, run the new model on the same dataset without rewriting the prompt, and change the instructions only for the differences you actually find. The release gate stays the same.

Finally, on how to read this book itself. Its statements are labeled by source, and the label is worth accounting for.

• Official - a direct recommendation or documented behavior in a primary source. Check the current page before adopting.
• General conclusion - a principle confirmed by materials from several companies. A strong starting hypothesis.
• Heuristic - an engineering synthesis based on the sources and architectural invariants. Be sure to confirm it with your own eval.

And a boundary of honesty.

The currency boundary. The review was done on July 31, 2026. Living documentation, model aliases, preview features, and deprecation timelines change. The book teaches a durable engineering model, but provider-specific details need rechecking before release.

The bottom line. Effective prompting in 2026 = a clear product contract + minimal high-signal context + well-designed tools + explicit state + bounded orchestration + a verifiable outcome.