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AI coding agents are unbelievable as there are -- but there are still tons of powerful techniques that will maximize the value you get from them.
These tips will save you hours and noticeably improve the accuracy and predictability of your coding agents.
1. Keep files short and modular
Too-long files are one of the biggest reasons for syntax errors from agent edits.
Break your code into small, self-contained files -- like 200 lines. This helps the agent:
Grasp intent and logic quickly.
Avoid incorrect assumptions or side effects.
Produce accurate edits.
Short files also simplify reviews. When you can scan a diff in seconds, you catch mistakes before they reach production.
2. Customize the agent with system prompts
System prompts are crucial for guiding the AI's behavior and ensuring it understands your intentions.
Before you even start coding, take the time to craft clear and concise system prompts.
Specify the desired coding style, architectural patterns, and any constraints or conventions your project follows.
Like for me I'm not a fan of how Windsurf likes generating code with comments -- especially those verbose doc comments before a function.
So I'd set a system prompt like "don't include any comments in your generated code".
Or what if you use Yarn or PNPM in your JS projects? Coding agents typically prioritize npm by default.
So you add "always use Yarn for NPM package installations".
On Windsurf you can set system prompts for Cascade with Global Rules in global_rules.md
3. Use MCP to improve context and capability
Connect the agent to live project data—database schemas, documentation, API specs—via Model Context Protocol (MCP) servers. Grounded context reduces hallucinations and ensures generated changes fit your actual environment.
Without MCP integration, you're missing serious performance gains. Give the agent all the context it needs to maximize accuracy and run actions on the various services across your system without you ever having to switch from your IDE.
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