Most checklists are either too vague to act on or too long to use. This one is built to sit beside you while you work. Every item is concrete, and every item comes with a one-line reason so you understand why it matters and can skip the ones that do not apply to your task. Run through it before you ship anything that depends on AI reasoning.
The checklist is grouped into four phases: deciding, prompting, verifying, and operating. Work top to bottom. Not every item applies to every task, and the justifications tell you when to use judgment.
Phase 1: Decide Whether to Reason
Before writing a single prompt, decide if reasoning is even warranted.
- [ ] Confirm the task has multiple dependent steps. Reasoning pays off only when steps build on each other; on single-step tasks it adds cost and risk.
- [ ] Rule out a direct answer. If a lookup, classification, or short summary would do, use that instead and skip reasoning entirely.
- [ ] Match rigor to stakes. A casual question needs none of the heavy machinery below; a billing calculation or legal interpretation needs all of it.
- [ ] Decide between asking and using a reasoning model. A reasoning-tuned model reasons by default but costs more in latency; pick based on volume and stakes.
If you are unsure where the line falls, our Complete Guide lays out the trade-offs.
Phase 2: Structure the Prompt
Once you have decided to reason, structure the prompt so reasoning actually helps.
- [ ] Require reasoning before the answer. An answer stated first turns reasoning into rationalization; ordering matters because the model reads left to right.
- [ ] Forbid premature conclusions explicitly. A line like "do not state your answer until you have worked through every step" enforces the order.
- [ ] Separate reasoning from the final answer. Mark the answer clearly so you can parse it or hide the reasoning from users.
- [ ] Provide a worked example if format matters. A single example anchors the structure when another system will parse the output.
- [ ] Decompose complex tasks into named sub-steps. Breaking a hard problem into stages makes each one inspectable and the whole thing more reliable.
The step-by-step approach shows these prompt moves in sequence.
Quick prompt sanity check
Before you run the prompt, scan it against three fast questions:
- Does the reasoning come before the answer, with no conclusion stated up front? If not, fix the order first.
- Is the final answer clearly marked so you can find and parse it? If it is buried, add a delimiter.
- For a hard task, did you break it into sub-steps rather than asking for one giant leap? If not, decompose.
These three take seconds to check and catch the prompt-level mistakes that cause the most downstream failures. Make them a reflex before any reasoning run.
Phase 3: Verify the Result
This phase is where most failures get caught. Do not skip it for anything that matters.
- [ ] Check the answer, not the explanation. Fluent reasoning is not proof; verify the final result independently.
- [ ] Spot-check one or two intermediate steps. If a key step is wrong, the conclusion is suspect even if the prose reads well.
- [ ] Look for the swerve. Confirm the conclusion actually follows from the last reasoning step; this catches a large share of errors.
- [ ] Recompute exact figures with code. For arithmetic and dates, deterministic recomputation beats trusting the model's math.
- [ ] Use self-consistency for high-stakes single answers. Run several passes and take the majority answer where one correct answer exists.
These verification habits come straight from our best practices.
Phase 4: Operate at Scale
Once it works, make it sustainable.
- [ ] Measure accuracy with and without reasoning. Confirm reasoning actually improved results before paying for it on every request.
- [ ] Route easy cases to direct answers. Send only hard cases down the reasoning path to control latency and cost.
- [ ] Cap reasoning length. Prevent the model from rambling, which wastes tokens without improving the answer.
- [ ] Cache repeated queries. Do not pay to reason through the same question twice.
- [ ] Hide raw reasoning from users by default. Show a clean answer; expose reasoning only when it is the value you provide.
- [ ] Add a fallback for low-confidence cases. When verification flags a mismatch, escalate to a human rather than shipping a wrong answer.
Phase 5: Review and Improve Over Time
A checklist is not a one-time gate. The best teams revisit it as their models, tasks, and volumes change.
- [ ] Re-run accuracy checks after any model change. A technique that helped on one model version may add nothing on another; re-measure rather than assume.
- [ ] Audit a sample of production reasoning traces. Periodically read real outputs to catch failure patterns that test sets miss.
- [ ] Track your most common failure mode. Knowing whether you mostly suffer from swerves, misreads, or overlong chains tells you where to invest.
- [ ] Retire reasoning where it stopped helping. As models improve, some tasks no longer need explicit reasoning; drop it to reclaim speed and cost.
- [ ] Update your prompt library with what worked. Save proven patterns so the next task starts from a known-good baseline rather than scratch.
This phase keeps the checklist alive. Reasoning quality drifts as inputs and models change, and a periodic review catches the drift before it shows up as bad answers in production.
How to Use This Checklist
Treat the four phases as a pipeline. You can run the whole thing for a high-stakes production feature, or just the first two phases for a one-off question. The justifications let you make that call deliberately rather than skipping steps out of haste. When models or tasks change, revisit Phase 4, because what was measured as helpful before may not hold. Our common mistakes article covers what goes wrong when items here get skipped.
Frequently Asked Questions
Do I have to complete every item every time?
No. The checklist scales with stakes. For a casual question, the first two phases are plenty. For a production feature that affects money or trust, work through all four. The justifications tell you which items your task actually needs.
What is the most important phase?
Verification. Most damaging failures are confident wrong answers, and the verification phase is what catches them. If you are short on time, never skip checking the final answer independently.
How often should I revisit the operating phase?
Whenever the underlying model or the task changes. A reasoning technique that measurably helped on one model version may add no value on another, so re-run your accuracy measurement after any significant change.
Can I use this checklist without writing code?
Mostly yes. The deciding, prompting, and most verification items are pure prompt design and review. Only the deterministic recomputation and some operating items, like caching and routing, require engineering, and those apply when you are building a system rather than asking one-off questions.
Why include a justification for each item?
Because a rule you do not understand is a rule you will misapply or follow blindly. The justifications let you decide when an item applies, skip the ones that do not, and adapt the checklist to your situation rather than treating it as dogma.
Key Takeaways
- Decide whether reasoning is warranted before prompting; skip it for single-step tasks.
- Structure prompts so reasoning comes before the answer and is clearly separated from it.
- Verification is the phase that catches confident wrong answers; never skip it for important work.
- Operate at scale by measuring, routing, caching, and adding a human fallback for low-confidence cases.
- Scale the checklist to your stakes, using the per-item justifications to decide what applies.