Prompt Templates for Debugging Production Issues

Use AI for production debugging as an evidence-to-validation workflow: collect facts, form hypotheses, verify them, make the smallest supported fix, and validate safely. Do not ask an assistant to patch a live problem from a vague symptom and apply the result blindly.

The templates below keep the investigation grounded. Remove secrets, access tokens, personal data, and customer identifiers before you share logs or traces.

The production debugging sequence

evidence -> hypotheses -> verification -> smallest fix -> validation

Every prompt should include the expected behavior, observed behavior, environment, reproduction information, relevant recent changes, and the safe limit of the requested work.

Template: initial incident triage

We need to investigate a production issue. Do not propose a code change yet.

Symptoms: [What users or monitoring show.]
Expected behavior: [What should happen.]
Observed behavior: [What actually happens.]
Impact: [Who is affected, frequency, severity, and safe fallback if known.]
Environment: [Version, deployment, browser/OS, region, feature flags, or
runtime details.]
Recent changes: [Relevant deploys, migrations, configuration, or dependencies.]
Evidence: [Sanitized logs, errors, metrics, trace IDs, screenshots, commands.]
Reproduction: [Reliable steps, or state that it is intermittent.]

Return:
1. a concise timeline of confirmed facts;
2. the top hypotheses ordered by likelihood and impact;
3. the next safe observation or reproduction step for each hypothesis;
4. any missing information that materially blocks diagnosis.

This gives an assistant enough material to reason without pretending it has access to your production systems.

Template: reproduce before fixing

Attempt to reproduce this issue locally or in the approved non-production
environment.

Expected: [Expected result.]
Observed: [Actual result.]
Setup: [Fixture, account state, command, configuration, or test data.]
Recent change to investigate: [Commit, release, or behavior change.]

Do not edit production code until the failure is reproduced or we have an
evidence-based explanation for why reproduction is not possible. Capture the
smallest failing test, log sequence, or command output that distinguishes the
leading hypotheses.

If reproduction is impossible, state the uncertainty and recommend safe instrumentation or a rollback decision—not a speculative code patch.

Template: analyze sanitized logs

Analyze these sanitized logs for a production failure.

Context: [Service/feature and expected request flow.]
Time window: [Start/end and timezone.]
Expected behavior: [Expected success path.]
Observed behavior: [Error or degraded behavior.]
Recent changes: [Deploy/configuration/dependency changes.]
Logs: [Sanitized entries in chronological order.]

Separate facts from inferences. Correlate events only where IDs, timestamps, or
causal evidence support it. List plausible hypotheses, the log or metric that
would confirm each, and the next lowest-risk observation.

Do not let an assistant treat neighboring timestamps as proof of causality.

Template: investigate a command or script failure

Investigate why this production maintenance command failed.

Command: [Exact command with secrets removed.]
Expected output: [Expected success signal.]
Observed output: [Exact sanitized stderr/stdout and exit code.]
Environment: [Shell, working directory, OS/runtime version, relevant files.]
Recent changes: [Script or deployment changes.]

First explain what the command actually does and identify the earliest failing
step. Propose safe diagnostic commands that do not mutate data. Only after the
failure is understood, propose the smallest fix and how to validate it in a
non-production environment.

For terminal failures, exact input and output are often more valuable than a prose summary.

Template: investigate a rollout regression

Assess whether this regression is related to the recent rollout.

Baseline: [Behavior/version before the rollout.]
Change: [Release, flag, migration, or configuration difference.]
Observed regression: [Who is affected and how.]
Evidence: [Metrics, logs, traces, screenshots, or a reproduction.]
Constraints: Do not change the rollout or migrate data until the evidence
supports a decision. Preserve the rollback path.

Compare the baseline and changed paths. Identify the smallest experiment,
feature-flag check, or non-production test that could confirm causation.
Recommend rollback, mitigation, or code investigation with the confidence and
tradeoffs for each.

The right answer may be a rollback or a feature flag adjustment, not a code change.

Template: request a safe fix

Only use this after the evidence supports a cause:

Confirmed cause: [Evidence-backed root cause.]
Scope: [Files, module, or configuration to change.]
Behavior to preserve: [Existing success path, public contract, permissions,
data, locale, performance, or rollback behavior.]

Propose the smallest fix. Include:
- why it addresses the confirmed cause;
- the regression test or reproducible check;
- validation in an approved non-production environment;
- rollout, monitoring, and rollback considerations;
- behavior that remains uncertain.

Do not include unrelated refactoring in this change.

The prompt keeps a real incident from becoming an opportunity for an unreviewable rewrite.

Template: validate after the fix

Validate the fix against the original incident.

Check:
1. the original reproduction or failing test now passes;
2. the normal success path remains unchanged;
3. relevant error and edge paths still behave safely;
4. deployment or monitoring signals show the expected recovery;
5. the rollback condition remains available.

Report the evidence for each check and explicitly name anything not verified.

Validation should answer the incident’s original question, not merely confirm that a new code path executed.

Use AI as a disciplined investigator

AI can help organize evidence, generate hypotheses, explain unfamiliar code, and suggest tests. It cannot replace access control, incident ownership, production safeguards, or human review.

Start with how to write better AI prompts for the general structure. Use AI coding assistant mistakes and how to avoid them to recognize unsafe shortcuts, and use prompt templates for refactoring code only after the incident is understood.

If the evidence includes shell commands, reproduce the workflow safely before changing it. Practice command-line scenarios in the browser to build the habit of checking input, output, and failure signals.