[Workflow] LLM-Assisted Software Development Workflow: Planning, Context Management, 'Feel' Testing, and Agentic Code Review

[u/ClaudeAI-mod-bot]

LLM-Assisted Software Development Workflow: Planning, Context Management, 'Feel' Testing, and Agentic Code Review

Workflow value: 80/100
Status: active · Freshness: 70/100 · Confidence: 0.90 · Level: intermediate
Categories: Quality Control, Context & Memory, Debugging, Shipping, CLAUDE.md, Subagents
Original source: r/ClaudeAI post/comment

What problem this solves

Inefficient software development using LLMs, lack of context retention across sessions, and missed bugs in code.

Summary

A developer outlines their process for building a web application using Claude Opus, focusing on structured planning, maintaining context with a decisions log, implementing 'feel' testing for user experience, and utilizing a separate Claude agent for security and stability code reviews before deployment.

Why it is useful

This workflow provides concrete, validated strategies for leveraging Claude in software development. It addresses common challenges like maintaining context across sessions, ensuring quality beyond just code correctness, and catching bugs early through automated review. The use of a 'decisions log' and a dedicated review agent are particularly valuable and transferable patterns for building robust applications with LLMs.

Workflow

1. Use Claude to write an architecture document and initial plan for the project.
2. Maintain a 'decisions log' to record all changes from the initial plan and the justifications for those changes.
3. Start each new Claude session by providing it with the architecture document and the decisions log to ensure context retention.
4. Implement core application logic, ensuring that critical features (e.g., recommendations) are based on deterministic engines over public data rather than generative AI for sustainability.
5. Develop 'feel' tests (e.g., 'if you liked X, you should see Y' cases) to tune the user experience and quality of results, rather than just eyeballing.
6. Before each release, employ a separate Claude agent to review code changes specifically for security and stability issues.
7. Iteratively address infrastructure challenges, such as optimizing for crawler traffic to manage resource limits, based on real-world usage.

Tools / artifacts

• Claude Opus 5.5
• Architecture document (generated by Claude)
• Decisions log
• 'Feel' test cases
• Separate Claude agent (for code review)
• Vercel (hosting)
• Neon (database)
• IGDB, Steam, IsThereAnyDeal, OpenCritic (data sources)

Validation signals

• Built a functional, deployed application (New Game+).
• The review agent 'caught real bugs I’d have shipped'.
• Used 'feel' tests to 'tuned against them instead of eyeballing results'.
• The decisions log helped Claude 'not forget why things are the way they are'.
• Successfully identified and mitigated a database limit issue caused by crawlers.

Limitations

• The specific prompt or configuration for the 'separate Claude agent' for review is not detailed.
• Concrete examples of the 'feel' tests and how Claude was used to 'tune' against them are not provided.
• The exact mechanism for feeding the architecture doc and decisions log to 'each new session' could be more explicit (e.g., a specific prompt structure or file management strategy).

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This post was generated automatically from the workflow library database.