{"slug": "how-i-use-ai-as-a-senior-engineer", "title": "How I Use AI as a Senior Engineer", "summary": "A senior engineer has developed a structured prompting system for AI code reviews that identifies bugs, security holes, and actionable fixes, moving beyond generic feedback. The engineer found that most developers use AI incorrectly for code reviews by asking vague questions, and created a three-part framework—role, framework, and structured output—to produce senior-engineer-level results. The system includes five copy-paste-ready prompts for pull request reviews, security audits, performance analysis, and debugging.", "body_md": "*Tags: #ai #codereview #productivity #webdev*\n\nI've been using AI for code reviews for over a year. In that time I've learned one uncomfortable truth:\n\n**Most developers are using AI wrong for code reviews.**\n\nThey paste code and ask \"is this good?\" They get back a wall of generic feedback that could apply to literally any codebase. It feels useful for about 10 seconds, then you realize nothing actionable came out of it.\n\nThe problem isn't the AI. It's the prompt.\n\nAfter hundreds of iterations, I've identified the patterns that separate a mediocre AI code review from one that actually finds bugs, catches security holes, and suggests fixes a senior engineer would be proud of.\n\nHere's what I learned — and the exact prompts I now use daily.\n\nWhen you ask AI a vague question, you get a vague answer. This is especially true for code review because the AI has no context about:\n\nA great code review prompt gives the AI a **role**, a **framework**, and a **specific output format**. When you do that, the results are night and day.\n\nEvery high-quality code review prompt has three parts:\n\n**1. A role** — \"You are a senior engineer with 15 years of experience...\"\n\nThis primes the model to respond with depth and specificity, not generic advice.\n\n**2. A framework** — tell it exactly what to look for and in what order.\n\nSecurity issues? Performance? Readability? All three? Be explicit.\n\n**3. A structured output** — \"For each issue provide: severity, line reference, what's wrong, and a fix.\"\n\nWithout this, you get an essay. With it, you get an actionable list.\n\nHere are five from my collection. These are copy-paste ready — just fill in the brackets.\n\nUse this as your default. It gives you a structured pull request review with severity levels and concrete fixes.\n\n```\nYou are a senior software engineer with 15+ years of experience. Review the following code as if you were doing a thorough pull request review for a production system.\n\nFor each issue you find, provide:\n1. Severity (Critical / Major / Minor / Nitpick)\n2. Line or section reference\n3. What's wrong and why it matters\n4. A concrete fix with example code\n\nAfter the issue list, give an overall score (1–10) and a 2-sentence summary of the code's strengths.\n\nCode to review:\n[PASTE CODE HERE]\n\nLanguage/framework context: [e.g. Python 3.11, FastAPI]\n```\n\nThis one's saved me twice from shipping auth bugs to production.\n\n```\nAct as a security-focused code auditor. Analyze the following code specifically for security vulnerabilities.\n\nCheck for (but don't limit yourself to):\n- Injection attacks (SQL, command, LDAP)\n- Authentication/authorization flaws\n- Sensitive data exposure\n- Hardcoded secrets or credentials\n- Input validation gaps\n\nFor each vulnerability found:\n- Name the vulnerability type (use OWASP naming where applicable)\n- Explain the attack vector\n- Show the vulnerable code snippet\n- Provide a secure replacement\n\nEnd with a risk rating: Low / Medium / High / Critical.\n\nCode:\n[PASTE CODE HERE]\n```\n\nPerfect for spotting N+1 queries and O(n²) nightmares before they hit prod.\n\n```\nYou are a performance engineering expert. Analyze the following code for performance issues and optimization opportunities.\n\nFocus on:\n- Time complexity (provide Big-O analysis)\n- Unnecessary loops or redundant operations\n- Database query inefficiencies (N+1 problems, missing indexes)\n- Caching opportunities\n\nFor each issue: explain the problem, estimate the performance impact (low/medium/high), and show an optimized version.\n\nCode:\n[PASTE CODE HERE]\n\nContext: [expected data volume, latency requirements]\n```\n\nWhen you're stuck on a bug and need a structured way out.\n\n```\nI'm going to describe a bug. Help me debug it systematically like a senior engineer who has seen everything.\n\nWalk me through:\n1. Likely root causes ranked by probability\n2. Specific questions I should answer to isolate the issue\n3. Diagnostic steps in order\n4. Common gotchas in this area I might be overlooking\n5. The most likely fix based on the symptoms\n\nBug description: [DESCRIBE THE BUG]\nRelevant code: [PASTE CODE IF AVAILABLE]\nEnvironment: [language, framework, recent changes]\n```\n\nFor bugs that keep coming back. Gets to the real issue, not just the symptom.\n\n```\nApply the \"5 Whys\" technique to this bug to find the true root cause.\n\nWalk through:\nWhy 1: Why did the bug happen? (immediate cause)\nWhy 2: Why did [Why 1] happen?\n...and so on to Why 5.\n\nThen provide:\n- The systemic fix that prevents recurrence\n- Process or tooling improvements to catch this class of bug earlier\n\nBug description: [DESCRIBE THE BUG IN DETAIL]\n```\n\n**Provide context generously.** The more the AI knows about your stack, team size, and constraints, the more relevant its feedback.\n\n**Ask for one thing at a time.** Don't ask for a security review AND a performance review AND a readability audit in one prompt. Run them separately and compare.\n\n**Iterate on the output.** If a suggestion doesn't fit your codebase, push back: \"That fix won't work because we use X. What's an alternative?\"\n\n**Use the feedback to learn.** When AI flags something you didn't notice, understand *why* it's a problem. This is how your actual engineering instincts improve over time.\n\nI've put together a full pack covering:\n\nAll model-agnostic — works with Claude, ChatGPT, Gemini, or whatever you use.\n\nGrab the full pack here: **[ MY LEMON SQUEEZY LINK]**\n\n*If this was useful, drop a ❤️ or share it with your team. Happy shipping.*", "url": "https://wpnews.pro/news/how-i-use-ai-as-a-senior-engineer", "canonical_source": "https://dev.to/kengineering/how-i-use-ai-as-a-senior-engineer-35ie", "published_at": "2026-05-25 23:08:20+00:00", "updated_at": "2026-05-25 23:33:36.348933+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-tools", "large-language-models"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/how-i-use-ai-as-a-senior-engineer", "markdown": "https://wpnews.pro/news/how-i-use-ai-as-a-senior-engineer.md", "text": "https://wpnews.pro/news/how-i-use-ai-as-a-senior-engineer.txt", "jsonld": "https://wpnews.pro/news/how-i-use-ai-as-a-senior-engineer.jsonld"}}