← Use Cases

AI-Assisted Software Development Workflow

An example multi-model workflow for planning, implementing, and reviewing software with AI support

Industry general
Complexity intermediate
Tools: claude code , cursor , ollama , hugging face
Models: claude opus 4 6 , gpt 5 , gpt 5 codex , gemini 3 pro preview
development workflow chatgpt codex code-review productivity
Updated February 15, 2026

The Challenge

A common software-team challenge is uneven execution speed: simple tickets still take too long because engineers constantly switch between scoping, coding, and review. Quality can also vary. Under deadline pressure, teams often spend too little time on edge cases, regression checks, and test updates.

Typical pain points include:

  • Features start without clear acceptance criteria.
  • Multi-file changes are slower than expected and prone to inconsistencies.
  • Pull request reviews drift toward style comments instead of correctness and risk.

The goal of this use case is not “AI writes everything.” The goal is to reduce repetitive work so engineers can focus on architecture, product tradeoffs, and production risk.

Suggested Workflow

Use a staged, role-based AI workflow where each model has one clear job:

  1. Planning pass in ChatGPT (GPT-5): define scope, constraints, and acceptance criteria.
  2. Implementation pass in Claude Code or Cursor: make repository-aware edits in small steps.
  3. Refactor/test pass with GPT-5 Codex: tighten structure and propose focused tests.
  4. Local verification pass: run build/tests and use Ollama when code cannot leave local environment.
  5. Independent review pass with a second model (for example Claude Opus or Gemini): challenge assumptions before human merge review.

This role split keeps prompts short, lowers ambiguity, and makes outputs easier to evaluate.

Implementation Blueprint

Apply this loop for any non-trivial ticket:

1) Build a mini spec (problem, constraints, done criteria)
2) Generate implementation plan
3) Execute in small commits
4) Run build + tests
5) Ask for risk-focused review

Practical setup details:

  • Keep a repository instruction file so assistants follow architecture and naming conventions.
  • Maintain separate prompt templates for planning and review.
  • Require either test updates or explicit “no test change needed” reasoning.
  • Run bun run build before opening PRs.

Example planning prompt:

You are helping implement a feature in an Astro + React + TypeScript codebase.
Task: [ticket summary]
Constraints: [performance/security/conventions]
Return:
1) files to change
2) risks and edge cases
3) step-by-step implementation plan
4) test plan

Example review prompt:

Review this diff for correctness, regression risk, security impact, and missing tests.
Prioritize findings by severity and give concrete fixes.

Potential Results & Impact

If this workflow is used consistently, teams can reasonably expect:

  • Faster path from ticket acceptance to first PR.
  • Fewer review cycles because scopes and diffs are clearer.
  • Better test hygiene on modified files.
  • Less developer fatigue on repetitive tasks.

Track impact with a short metrics set: lead time to PR, review cycles per PR, post-merge defect rate, and percentage of PRs with test updates.

Risks & Guardrails

Likely failure modes:

  • Overloaded prompts that mix planning, coding, and documentation in one request.
  • Low-quality generated tests that miss edge conditions.
  • Overtrusting model feedback without repository validation.

Guardrails that keep this workflow reliable:

  • One objective per prompt.
  • Mandatory assumptions/unknowns section in planning output.
  • Human approval for architecture changes and all merge decisions.
  • Build/tests required before review handoff.

AI can accelerate execution, but reliability depends on process discipline and explicit review gates.

Tools & Models Referenced

  • Claude Code (claude-code): Strong repository navigation and multi-file edits.
  • Cursor (cursor): Fast in-editor iteration and assistant-driven code changes.
  • Ollama (ollama): Local inference for sensitive snippets and offline experimentation.
  • Hugging Face (hugging-face): Quick access to benchmark datasets and model references.
  • Claude Opus 4.6 (claude-opus-4-6): Secondary reasoning pass for review and architecture checks.
  • GPT-5 (gpt-5): Planning, summarization, and acceptance-criteria drafting.
  • GPT-5 Codex (gpt-5-codex): Focused coding and refactor support at diff level.
  • Gemini 3 Pro Preview (gemini-3-pro-preview): Optional third perspective for cross-checking solution quality.