The current noise around AI tools is exhausting because every new model claim sounds like a revolution, making it hard to actually pick a stable workflow. The reality is that we've hit a plateau where most top-tier LLMs are competent enough for 90% of coding tasks, and the "best" tool is usually just the one that fits your existing IDE without adding friction.
For those struggling to get consistent results from their agents, I've found that a structured system prompt helps the AI stop guessing and start executing. Here is the exact prompt I use to keep my LLM focused on clean, production-ready code:
I've stripped my setup down to focus on utility over hype. Here is the current breakdown of my real-world AI workflow:
IDE: VS Code withCursor. While standard Copilot is fine, Cursor's indexing of the local codebase makes the context window actually useful.LLM Agent:ClaudeCode. For terminal-based iterations and complex refactors, this is currently light years ahead of standard chat interfaces.UI Scaffolding: Tailwind CSS + shadcn/ui. I don't waste time on custom CSS anymore; the AI generates these components perfectly.Cloud/Deployment: Vercel. The integration is too seamless to justify switching.Prompting Strategy: I use a "Context-First" approach. Instead of vague requests, I feed the agent the specific file structure and the desired outcome in a single shot.
For those struggling to get consistent results from their agents, I've found that a structured system prompt helps the AI stop guessing and start executing. Here is the exact prompt I use to keep my LLM focused on clean, production-ready code:
Act as a Senior Full-Stack Engineer. When proposing code changes:
1. Analyze the existing project structure to ensure naming conventions are consistent.
2. Prioritize readability and maintainability over "clever" one-liners.
3. If a change impacts multiple files, provide a clear execution plan before writing code.
4. Always include brief comments for complex logic.
5. Use TypeScript for all frontend code to ensure type safety.
Output format:
- Analysis: [Short explanation of the problem]
- Solution: [The code block]
- Verification: [How to test if the fix works]
In my experience, the vendor matters very little for basic autocomplete or boilerplate generation. Whether it's GPT-4o or Claude 3.5, the output is similar. The real difference comes from the tooling—specifically how the tool accesses your local files (RAG) and how you structure your prompt engineering to limit hallucinations. Keep it simple and focus on the shipping, not the subscription.
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All Replies （4） #
C
I'm still rocking Neovim, though I jump into VSCodium with the plugin every now and then. Honestly, I barely write actual code anymore since I just steer Claude Code to handle the heavy lifting. Most of my time is just spent hanging out in iTerm2.
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N
I'm running a similar setup on Arch, but I've actually stayed away from AI plugins to keep things lean. Just VS Code and the necessary runtimes. Do you find that the CLI tools for Claude handle the workflow better than the integrated IDE plugins?
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T
Does this actually hold up on larger codebases, or just small scripts? I'm still seeing hallucinations.
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A
I've noticed the same thing. Do you think better prompting helps or is it just a model limitation?
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