I’ve been using ChatGPT and similar large language models as part of my daily developer toolkit for over a year now. They’re fast, convenient, and—when used well—surprisingly capable. But can ChatGPT actually replace your coding assistant? The short answer: not entirely. The longer answer: yes for many everyday tasks, but no for deep architecture thinking, hands-on debugging in production, or ownership of critical design decisions.

What I ask ChatGPT to do for me

In my testing and real-world use at Techtoinsider, I rely on ChatGPT for a handful of high-value developer tasks:

  • Explaining concepts quickly (e.g., "difference between optimistic and pessimistic locking").
  • Scaffolding code snippets, templates, or config files (CI pipelines, Dockerfiles, unit test skeletons).
  • Refactoring suggestions and small performance tweaks.
  • Translating code between languages (Python -> TypeScript, Java -> Kotlin).
  • Generating test cases, mock data, or sample inputs for edge-case coverage.
  • Reviewing code for obvious anti-patterns and security red flags (SQL injection patterns, unsafe deserialization).

    • These are tasks where the cost of an occasional error is low, the turnaround needs to be fast, and the model’s general knowledge shines.

    Prompts that get reliable, useful results

    How you prompt ChatGPT matters more than which model you use. Here are patterns I use that consistently produce practical, actionable outputs.

  • Be explicit about the environment: "I’m using Node 18, TypeScript 5.2, and Express 4. Create a route that validates a JSON body with Zod and returns 422 on validation error."
  • Request structure: "Give me a short explanation, then a complete code example, then a brief test case." This reduces follow-ups.
  • Ask for trade-offs: "Suggest three approaches with pros and cons and when to use each."
  • Limit scope: "Focus only on input validation and error handling—no database logic."
  • Iterative refinement: Start with a scaffold prompt, then ask: "Simplify variable names and add inline comments aimed at a junior engineer."

    • Example prompt I use daily:

  • "Write a simple Flask REST endpoint /upload that accepts multipart file upload up to 10MB, saves to AWS S3 using boto3, and returns the S3 URL. Include error handling and sample pytest unit tests that mock S3."

    • Prompt examples you can copy

    Copy-paste these and tweak to your stack:

  • Scaffold a feature: "Generate a full Python 3.11 project skeleton for a CLI app that reads a CSV, validates rows, and writes normalized JSON. Include setup.cfg, requirements.txt, a main.py, and a sample pytest file."
  • Refactor request: "Refactor the following function to be more readable and reduce cyclomatic complexity. Keep unit tests passing. Explain the refactor in 3 bullet points."
  • Security review: "Review this Express middleware for XSS/CSRF/SQL injection vulnerabilities. Highlight lines of concern and propose fixes."

    • Where ChatGPT often gets it wrong

    It's important to be realistic. I’ve logged a lot of errors while using ChatGPT in production-like scenarios:

  • Fabricated facts or APIs: The model can invent functions, parameters, or nonexistent library features. Always double-check imports and function signatures.
  • Outdated or inaccurate code idioms: It may suggest deprecated APIs (old React lifecycles, legacy Python 2 patterns). Specify versions to reduce this.
  • Overconfident security advice: It can miss subtle attack vectors or recommend incomplete fixes. Treat its security recommendations as starting points, not final reviews.
  • Stateful debugging limitations: The model doesn’t have access to your runtime, logs, or breakpoints (unless you paste them). Complex, multi-step bug hunts that require reproducing issues are still human-led work.
  • Architecture and tradeoff nuance: For high-stakes architecture (microservice boundaries, event-driven systems at scale), the model’s suggestions lack organizational context and often ignore operational realities like SLOs, cost, and team skills.

    • Best practices to minimize mistakes

    These are habits I recommend adopting if you want to use ChatGPT as an effective assistant without introducing risk.

  • Pin versions: Always state language/runtime/library versions in your prompt.
  • Ask for citations or exact import lines: "List exact pip/npm install commands and import lines required."
  • Run and test everything: Treat generated code as a draft. Execute, lint, and run tests.
  • Use small, reproducible contexts: When debugging, paste minimal code and error traces. The model performs far better with focused inputs.
  • Combine with static tools: Use linters, SAST tools, type checkers (mypy, TypeScript), and CI pipelines to catch issues the model misses.

    • When ChatGPT is actually better than a human assistant

    There are situations where ChatGPT outperforms a typical human teammate in speed or breadth:

  • Quick prototyping across unfamiliar stacks (generate idiomatic snippets for a new language).
  • Generating numerous variations fast (A/B copy for UIs, many SQL query shapes).
  • Onboarding docs and code comments: it can rapidly translate technical intent into clear prose.

    • When you shouldn’t rely on it

    Don’t hand over full ownership of:

  • Security-critical code that handles authentication, encryption keys, or payment data without an expert review.
  • Production migrations, major refactors, or anything that requires systems-level testing or load testing.
  • Decisions that need domain-specific regulatory compliance (HIPAA, PCI-DSS). ChatGPT can help summarize guidelines but cannot certify compliance.

    • Practical workflow I use

    Here’s a real workflow that mixes ChatGPT with human discipline:

  • 1) Draft: Use ChatGPT to generate scaffold or first draft code.
  • 2) Local tests: Run unit tests and linters locally. Fix trivial issues.
  • 3) Code review: Submit to PR and request a human reviewer—use the model’s suggested review comments as an aide, not a replacement.
  • 4) Security scan: Run SAST and dependency checks (Snyk, Dependabot, GitHub Advanced Security).
  • 5) Staging: Deploy to staging and run integration/load tests before production.

    • Quick comparison table: where ChatGPT helps vs where humans are needed

    Task ChatGPT strength Human essential
    Scaffold & snippets Fast, multi-stack Edge-case correctness
    Bug triage Suggests hypotheses Reproducing & testing in env
    Design & architecture Offers options & high-level pros/cons Context-aware tradeoffs, SLOs
    Security review Finds obvious patterns Deep pen-testing & compliance

    At this stage, I treat ChatGPT like a very knowledgeable teammate who speeds up routine tasks, drafts, and ideation. It’s transformative for productivity, but it’s not a fully independent replacement for an experienced developer or security engineer. Use it to accelerate, not absolve responsibility. When you combine clear prompts, version pinning, automated checks, and human judgment, ChatGPT becomes a force multiplier rather than a risk.