{"id":22002,"date":"2025-09-10T15:00:32","date_gmt":"2025-09-10T14:00:32","guid":{"rendered":"https:\/\/www.equalexperts.com\/?p=22002"},"modified":"2025-09-15T11:23:37","modified_gmt":"2025-09-15T10:23:37","slug":"ai-assisted-development-workflow","status":"publish","type":"post","link":"https:\/\/www.equalexperts.com\/blog\/our-thinking\/ai-assisted-development-workflow\/","title":{"rendered":"From specification to code: A practical AI-powered workflow for developers"},"content":{"rendered":"

A step-by-step report on how we integrated LLMs into our feature delivery process \u2014 without losing control<\/em><\/p>\n

Why this matters<\/h2>\n

AI is already reshaping how we write code. But while large language models (LLMs) can generate working software fast, they also raise questions about quality, control, and predictability. One-shot prompts that go straight from spec to code often miss key details or make subtle assumptions \u2014 costing you hours in clean-up later.<\/p>\n

This post offers a hands-on, repeatable workflow for using LLMs with precision. It separates planning from execution, introduces clear review gates, and helps you ship faster without sacrificing engineering discipline.<\/p>\n

This is a practical pattern used by one of our teams that might help you too.<\/p>\n

How it flows in practice<\/h2>\n\"A\n

We started by writing a planning prompt. The prompt links to the clear specification(s) and points to the files and patterns in the code to work with. From that prompt, the model returned an implementation plan that names the files to change and the shape of the edits. We reviewed and iterated on the plan until it was right. Only then did we ask the model to implement the code according to the approved plan. After a quick human review and any fixes, we asked the model to continue with the plan by implementing all required tests. Trim noisy\/duplicated tests, and when code and tests both look good, commit and open a Pull Request.
\nEvery step has a feedback loop. If something feels off, go one step back. Don\u2019t let mistakes compound.<\/p>\n

Tools we used<\/h3>\n

We ran this workflow using Claude Code (Opus 4 and Sonnet 4) inside IntelliJ IDEA, but any LLM that accepts project context from the command line will work. Claude had full access to source files \u2014 no context injection games required.<\/p>\n

A practical AI-powered workflow for developers<\/h2>\n

Step 1: From spec to implementation plan<\/h3>\n

We don\u2019t jump straight to \u201cgenerate code\u201d. First, we generate a plan. That plan is a concrete, reviewable proposal of how the model intends to implement the feature.<\/p>\n

Providing a clear, comprehensive specification is essential because it defines the feature in full. If anything is left out or ambiguous, the model will fill the gaps with its own assumptions \u2014 and those guesses often create unpredictable results and unnecessary rework. The specification is the authoritative statement of what to build; the planning prompt tells the model how to think about it by pointing to the right files, established patterns, and any constraints or details that must be considered.<\/p>\n

We take this extra step because one-shot prompts that generate code directly tend to wander. They introduce hidden assumptions, stray from established contracts, and produce noisy tests. A short implementation plan, by contrast, gives us a reviewable artifact before any code exists. It forces the scope to be explicit \u2014 which files to touch, which contracts to follow, which constraints to respect \u2014 and it often surfaces clarifying questions that improve the outcome.<\/p>\n

By reviewing the plan before code exists, we avoid expensive rework. Our results were cleaner diffs, faster iterations, and better alignment with architectural and business expectations.<\/p>\n

Anatomy of a good planning prompt<\/strong><\/p>\n

We use four sections:<\/strong><\/p>\n

#GOAL \u2013 Describe the feature and outcome in one short block. Keep it tight. Avoid trying to do too much at once.<\/p>\n

#ANALYSIS \u2013 Link to specs, list relevant files, name patterns to follow. Include non-functionals, APIs, flags, edge cases.<\/p>\n

#IMPLEMENTATION \u2013 Ask for a step-by-step implementation plan only. No code. Request edits by file, design decisions, and a focused test strategy.<\/p>\n

#CONSTRAINTS \u2013 Tell the model what not to do. No execution. Ask clarifying questions. Wait for human approval.<\/p>\n

Example: Planning a New API Endpoint<\/strong><\/p>\n

Here\u2019s a real planning prompt we used:\r\n# GOAL\r\nI want to implement a client verification endpoint: `GET \/clients\/{clientId}`.\r\n\r\n# ANALYSIS\r\n- Swagger: @docs\/client-api.yaml\r\n- Upstream: UserManagementService GET\/client, spec in @docs\/umg.yaml\r\n- Mappings: @doc\/get-client\/mapping.md\r\n- Add to: ClientController\r\n- New Service: ClientProfileService (follow patterns in ClientAccountService)\r\n\r\n# IMPLEMENTATION\r\nProvide an implementation plan including required changes to existing code and tests.\r\nSave as markdown in @docs\/get-client\/implementation-plan.md\r\n\r\n# CONSTRAINTS\r\n- Do not execute the plan\r\n- Ask clarifying questions if in doubt, don\u2019t make assumptions\r\n- Wait for human review\r\n<\/pre>\n
 <\/p>\n
Claude responded with a structured plan that looked something like this:<\/strong><\/p>\n
    \n
  • File-level changes \u2014 which files to update, classes to add, and methods to create<\/li>\n
  • Architecture flow \u2014 how controllers, services, and upstreams connect, with clear data mappings<\/li>\n
  • Execution order \u2014 phased steps that make dependencies and sequencing explicit<\/li>\n
  • Code examples \u2014 snippets showing DTO shapes, method signatures, or config templates<\/li>\n
  • Error handling \u2014 status codes, exception patterns, and edge cases to cover<\/li>\n
  • Testing strategy \u2014 unit vs. integration tests, with guidance on scope and data<\/li>\n<\/ul>\n
    Tip: We found Opus 4 slightly better for planning, but Sonnet 4 was often good enough \u2014 and significantly cheaper.<\/p>\n
    Step 2: Execute the plan (code only)<\/h3>\n
    Once the plan was approved, we asked Claude to generate code only, holding off on tests until later. This helped reduce noise and made the review more focused. We stuck to Sonnet 4 for code generation. Once a good plan exists, advanced reasoning isn’t usually required.<\/p>\n
    Review the code carefully<\/strong><\/p>\n
    Check that the code matches the plan exactly:<\/p>\n
      \n
    • Files and classes are correctly named<\/li>\n
    • Patterns and contracts are respected<\/li>\n
    • Business logic reflects the intent<\/li>\n
    • Edge cases are handled<\/li>\n
    • No files outside scope are touched<\/li>\n<\/ul>\n
      Once happy, we committed to a feature branch. That gave us a clean baseline before adding tests.<\/p>\n
      Step 3: Add tests and validate<\/h3>\n
      Using the same plan, we asked Claude to generate all required tests and validation logic. This step can be trickier. LLMs are more likely to produce redundant or superficial edge case tests here.<\/p>\n
      We reviewed:<\/p>\n
        \n
      • Test names match test logic<\/li>\n
      • Duplicates or no-ops are deleted<\/li>\n
      • Full coverage exists for the key flows<\/li>\n<\/ul>\n
        Once all tests passed and the logic checked out, we opened a PR.<\/p>\n
        Final thoughts: don\u2019t skip the plan<\/h2>\n
        The real power of this approach is its predictability. You move faster not by cutting corners, but by cutting rework. By separating planning from execution, you make every LLM output more deliberate, more reviewable, and more reliable.<\/p>\n
        Have you tried using LLMs this way? Got a workflow that works better? We\u2019d love to hear how you’re integrating AI into your software delivery \u2014 and swap lessons learned. Get in touch with us at Equal Experts.<\/p>\n
        Disclaimer:<\/strong>
        \nThis is the author\u2019s personal workflow, not an official Equal Experts methodology. We\u2019re not affiliated with any tools mentioned \u2014 we just use what works.<\/p>\n","protected":false},"excerpt":{"rendered":"
        A step-by-step report on how we integrated LLMs into our feature delivery process \u2014 without losing control Why this matters AI is already reshaping how we write code. But while large language models (LLMs) can generate working software fast, they also raise questions about quality, control, and predictability. One-shot prompts that go straight from spec […]<\/p>\n","protected":false},"author":56,"featured_media":22007,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[412,917,5],"tags":[930,931,932,933,934,935,655],"location":[],"class_list":["post-22002","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-experiment-with-us","category-our-thinking","tag-ai-assisted-development","tag-claude-code","tag-developer-workflow","tag-feature-delivery","tag-intellij","tag-practical-ai","tag-software-engineering"],"acf":[],"yoast_head":"\nFrom Specification to Code: An AI Workflow for Developers<\/title>\n<meta name=\"description\" content=\"A hands-on, repeatable AI-assisted development workflow that turns specifications into production-ready code and tests \u2014 without losing control.\" 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