How to Use Vibe Coding and LLMs to Write Reliable, Production Ready Code

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Vibe coding is a simple way of building software with the help of an LLM. You guide the model with clear instructions, it generates code and you refine it. This creates a fast loop that makes development quicker and more flexible.

It speeds up development, helps you test ideas faster and lets you build with more momentum. But many developers still raise concerns about LLM generated code. 

Common complaints include:

  • The model misunderstands requirements

  • Code quality is inconsistent

  • Long term maintenance becomes hard

  • Bugs slip through easily

LLM based coding is new and it comes with limitations. Still, you can get reliable, production ready code by following a few simple techniques. These tips work with any AI coding tool, including:

  • Cursor

  • GitHub Copilot

  • Google Antigravity

  • Windsurf

I use GitHub Copilot and Python, but these techniques apply to any coding agent or language.

Techniques to Improve Code Quality

Plan Before You Code

  • Write a clear plan before asking the agent to generate code

  • Share detailed requirements instead of vague goals

  • Break the project into small actionable steps

  • Use the LLM to help you turn requirements into tasks

Example

  • Instead of: "Build me a dashboard"

  • Say: "Create a dashboard with three pages: overview, metrics and settings. Use a light theme and chart components from the library I already use"

Ask the Agent to Confirm Understanding

  • After giving requirements, ask the agent to read, analyze and ask clarifying questions

  • This prevents assumptions and saves time later

Example

  • Add this line after giving requirements: "Read the requirements and ask questions if something is unclear"

Always Write Tests

  • LLM code often misses edge cases

  • Use separate LLMs for code and tests

  • Write unit tests and integration tests for every feature

  • Tests keep code aligned with real requirements

Example

  • Prompt to LLM 1: "Write a function that calculates discounts"

  • Prompt to LLM 2: "Write unit tests for the discount function covering zero, negative values and very large inputs"

Ground the Code with Official Documentation

  • Most agents can fetch documentation or run searches

  • Ask them to follow official docs for accuracy

  • Use MCP servers or tools for tasks like creating tables or schemas

Example

  • "Use the latest FastAPI documentation for building this endpoint"

  • "Follow SQLAlchemy official docs when creating models"

Generate Documentation for Each Feature

  • Create a markdown file for every feature

  • Let the LLM generate a short explanation of how the feature works

  • Read the doc after running tests so you understand the implementation

Example

  • "Create a markdown file explaining how the user signup flow works and list all steps the LLM followed"

Review All LLM Generated Code

  • Do not trust agent output blindly

  • Read the generated code and refine where needed

  • Final adjustments are usually minimal

Example

  • Ask the agent: "Explain why you wrote this block of code and whether there is a simpler alternative"

Use LLM Based Code Review Agents

  • Tools like CodeRabbit and GitHub Copilot Review help catch issues

  • Review code before merging to any branch

Use Git for Safe Iteration

  • Git helps you undo bad generations quickly

  • Resetting is faster than manually reverting files

  • Commit only after all tests pass

Example

  • Commit message: "Signup flow implemented. All tests passed"

  • If output is bad, reset to the previous commit


Apply Formatting and Static Analysis

  • Use tools like Black or other formatters

  • Add formatting and linting rules in your custom instructions

Example

  • "Format the code using Black and run flake8 linting"

Test Manually After Integration

  • Manual testing still matters

  • You will always catch some issues during hands-on testing

  • You make the final call, not the AI

Use Custom Instructions or Rules

  • Put all your coding rules in one file

  • Include these rules in every prompt

  • Cursor and GitHub Copilot both support custom instructions


Conclusion

  • AI coding agents improve fast but still need guidance

  • You are responsible for the commits, not the LLM

  • These guardrails help reduce hallucinations and improve stability

  • These techniques also reduce development cost, speed up delivery and increase confidence in the final output

  • Enjoy vibe coding and build ideas faster at lower cost

Do you use other techniques to improve coding agents or vibe coding? Share them below.

Location: Kochi, Kerala, India

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