🛠️🧰 Tips for Making LLMs Work for You
When I first tried using AI I cautiously tried to use it everywhere for everything. Over time I began to find ways to apply it that made me the most productive.
Below are some tips I’ve compiled while working with AI the past few years. It is by no means exhaustive~!
| Tip | Why It Helps | Quick Example |
|---|---|---|
| Focus on Smaller Problems | The larger your codebase the more the context window is degraded | Build boilerplate for a form that updates the user model |
| Start with a Clear Prompt | Reducing ambiguity, yields more accurate snippets. | Generate a Python function that parses ISO‑8601 dates |
| Validate with Tests | Catch hallucinations early. | Write a unit test that must pass before accepting the LLM's suggestion, write an e2e before you start the feature. Make new ones along the way. Make sure they all pass! |
| Always Verify Packages | LLM's will confidently give you a magic package that perfect for your problem, sometimes they don't exist! In the worst-case scenario they can link you to something with security vulnerabilities | Google the NPM package before you commit! Look at the commit history and look at the maintainers. |
| Assume Code was Written by a JR | This will challenge you to think critically about the context (LLMs don't), and surface issues | Read every line in your editor as if it were written by a JR |
| Iterate & Refine | Treat the model as a conversational partner. | Follow‑up: "Add error handling for invalid strings." Apply Fowler's Refactoring principles to evaluate trade‑offs. |
| Leverage Contextual Files | Upload relevant code files so the model sees the surrounding architecture, you'll get better results. | Use OpenAI's file upload feature with spreadsheets, integrate your issue tracker and wiki with MCP, use cursor rule files in each dir |
| Documentation & Knowledge Transfer | Living documentation helps teams go quicker | LLMs can auto‑generate API docs, inline comments, and migration guides. |
🤓 Good Engineering Practices
I’d like to add good engineering practices in here as well. They are helpful for all teams, especially ones that use LLM’s. They guide you towards higher levels of collaboration, productivity, & maintainability.
I find them to be imperative to do given the volume of code, re-work, and regressions that coding with AI creates.
| Tip | Why It Helps | Quick Example |
|---|---|---|
| Design Over Implementation | A good upfront design leads to long-term development advantages. | LLMs can help generate scaffolding but shouldn't have the final say in high‑level design. Create designs and architectural plans and present them to your team to look for holes and new ways to look at the problem |
| Do Smoke Tests Often | Catch issues early on | Run the app in the background and do a click through ever 15 minutes |
| Linting is More Important than Ever | Be sure to use linters deeply and expansively | Harness tools like EsLint, Pylint, and RuboCop |
| Stay Security‑Aware | Prevent accidental inclusion of secrets or vulnerable patterns. | Run static analysis on generated code. |
| Keep Pull Requests Focused | Always assume the code can and will have flaws. The more slop you let into the codebase, the harder it will be to clean up. | I like to come up with a team agreement to keep PR's 15 files or less or 750 lines or less. |
🤖👨🏽 Closing Thoughts – The Future of Human-AI Collaboration
Returning to the original question for this post is AI: “Autopilot or Co‑Pilot?”
Personally I think AI performs the role of something closer to co-pilot right now. It’s clear that software engineering will never been the same again. However, LLMs are augmentative, not a replacement for deep expertise. We don’t currently have agentic programmers, and we can’t get away with just vibe coding (at least not yet!).
In many ways the role of the senior / staff engineer is even more important than ever in this context. If we are using these tools for productivity boosts, we must also double down on ensuring our codebases are maintainable, performant, secure, and well architected.
I encourage folks to experiment with these tools evolve your best practices. As new tools and models come out we should continue to engage with them and find their best purposes.
Happy coding with your robot friends! 🎉