Splitting prompt responsibility improves AI skill reliability
Rim Zabarov describes how breaking a single monolithic AI prompt into distinct internal responsibilities — like input intake, implementation review, risk review, and quality check — produces more structured, actionable outputs for repeated developer tasks like PR reviews and bug fixes.
Score breakdown
Structuring AI coding prompts into distinct internal responsibilities — rather than accumulating rules in a single instruction — produces outputs where blockers, risks, and suggestions are clearly separated, making AI-assisted code review and bug triage more directly actionable.
- 01Rim Zabarov describes the pattern as an 'AI skill' — a repeatable AI workflow for one kind of work, such as a Codex skill, custom assistant, or system prompt.
- 02The core problem: a monolithic prompt forces the AI to handle input analysis, risk review, implementation review, and output formatting in a single pass.
- 03The proposed split divides internal work into six responsibilities: input intake, implementation review, action planning, risk review, quality check, and final editing.
Rim Zabarov traces the evolution of a developer-focused AI prompt that started simple — review a function, explain an error, suggest a plan — but grew complicated once real engineering tasks like PR reviews entered the picture. Each time the AI fell short, a new rule was added to the prompt: understand intent before suggesting a fix, check for missing context, consider risk. The prompt accumulated many individually reasonable rules, but the AI still had to satisfy all of them in a single pass, producing answers that sounded smooth but left the developer guessing which comments were blockers, which were suggestions, and what the AI had assumed versus confirmed.
The user still interacts with one skill and receives one answer; the structure is internal.
His solution was to treat the prompt not as one large text but as an "AI skill" — a repeatable workflow for one kind of work — and to split internal responsibilities within that skill. The split he describes for developer tasks includes: input intake (what was provided, what is missing, what cannot be assumed), implementation review (whether the change solves the stated problem), action planning (the smallest useful next step), risk review (data, permissions, compatibility, irreversible actions, user impact), quality check (tests, reproduction, evidence, uncertainty), and final editing (a concise, actionable answer). The user still interacts with one skill and receives one answer; the structure is internal.
Zabarov illustrates the difference with a concrete PR review example. A weak review produces a flat list — rename a variable, add a test, check permissions, improve readability — where a style comment sits alongside a potential security issue with no indication of priority. With the responsibility split, the same review surfaces a specific authorization failure that could return stale or unauthorized cached data as a clear blocker, separates open questions from suggestions, and ends with an explicit merge recommendation. The same pattern applies to bug-fix requests, where input intake separates confirmed facts from guesses before any patch is proposed.
Key facts
- 01Rim Zabarov describes the pattern as an 'AI skill' — a repeatable AI workflow for one kind of work, such as a Codex skill, custom assistant, or system prompt.
- 02The core problem: a monolithic prompt forces the AI to handle input analysis, risk review, implementation review, and output formatting in a single pass.
- 03The proposed split divides internal work into six responsibilities: input intake, implementation review, action planning, risk review, quality check, and final editing.
- 04The user still interacts with one skill and receives one answer; the responsibility split is internal to the prompt structure.
- 05A weak PR review example produces a flat, unprioritized list mixing style comments with potential security issues.
- 06With the split, the same review surfaces a specific authorization failure as a blocker, separates questions from suggestions, and ends with an explicit merge recommendation.
- 07The pattern also applies to bug-fix requests, where input intake separates confirmed facts from guesses before a patch is proposed.
Topics
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