Minimum Knowledge for AI Software Development
AI is a tool. It does not replace your knowledge of architecture or engineering.
Do not let AI make decisions for you. You must define all functional and non-functional requirements. Be specific. Focus on every detail.
Cheap models often lead to more work. They create errors that you must fix later. This wastes your time and the time of your team. For professional work, use high-reasoning models like Opus or GPT.
The tools you use matter. Use AI agents that run on your computer. The harness affects the quality of the output. For example, use Claude Code for Opus. Better tools extract better results from the same model.
Invest in professional plans. Cheap plans work for hobbies. Professional projects need the best models and high usage limits.
Every project needs a CLAUDE.md or AGENTS.md file. Keep it short. Write it in English. Include only essential project info.
Follow this workflow to avoid mistakes:
- Create an analysis document.
- Create an execution plan.
- Review the plan.
- Start implementation.
Your plan must include architecture, acceptance criteria, and automated tests. Be skeptical. Ask the AI to find gaps in the plan before it writes any code.
AI should only fail if it ignores the plan. It should never fail because you skipped the planning stage.
Human review is mandatory. You are responsible for every line of code in production. If the code is insecure or messy, it is your fault.
Your role is changing. You must move from a task implementer to an architect and tech lead. Think about the whole system while the AI handles the repetitive work.
Context is everything. One prompt is not enough. Provide business rules, architecture, and constraints to get better results.
Never work without tests. Ask the AI to write tests alongside the code. Always run tests, builds, and linters after every cycle.
AI speeds up execution, but it does not replace judgment. Your main job is now making good engineering decisions.
Do not accept code just because it works. Demand readability, security, and simplicity.
Use skills to standardize prompts in your company. This keeps quality and architecture consistent across all projects.
If planning and testing feels like too much work, do not use AI for development. Without these steps, you will create low-quality code and technical debt.
The responsibility stays with you. Do not blame the AI or the tools for bad code. Your company holds you accountable.
Source: https://dev.to/andredarcie/o-minimo-que-voce-precisa-saber-para-desenvolver-software-com-ia-1dc9
Optional learning community: https://t.me/GyaanSetuAi