A Developer’s Guide to AI-Assisted Coding: Tools, Workflows, and Best Practices

For an experienced developer, stepping into the world of AI-assisted coding can feel like learning a new paradigm. The key isn't just about which tool to use, but how to use it effectively to augment your skills without ceding control or understanding. Based on insights from developers actively using these tools, here is a guide to getting started.

Choosing Your AI Coding Assistant

There is no single "best" tool, as the landscape is constantly evolving, but a few names consistently rise to the top.

Cursor: A popular choice, often described as an "AI-first" code editor. It integrates tightly with various models and is frequently praised for its robust features that help manage context within a project.
Claude (specifically Claude Code): Often used within Cursor or via its own interface, Claude is highly regarded for its coding capabilities. It's frequently recommended for generating new code and following complex instructions.
GitHub Copilot: Integrated directly into IDEs like VS Code, Copilot is an excellent starting point. It's particularly strong at autocompletion and generating boilerplate code, making it an easy way to get a feel for AI assistance.
Terminal-Based Tools: For those who prefer the command line, tools like Aider and OpenCode offer a powerful alternative to IDE integrations, allowing for experimentation with different models.

Regarding pricing and subscriptions, the major players are generally reputable. However, a safe way to start is by using a service's API with a small amount of prepaid credit (e.g., $5-$10) rather than immediately signing up for a monthly plan. This lets you experiment without long-term commitment.

The Most Important Workflow: Context is King

The single most impactful strategy shared is the practice of creating and maintaining a central context file for your AI. Often referred to as CLAUDE.md, this document serves as the AI's guide to your project. Here's what to include:

High-level architecture: A summary of the project's structure.
Coding patterns and conventions: Specific rules or styles you want the AI to follow.
Project goals and constraints: The overall objectives and any limitations.

When you begin a task, you instruct the AI to reference this file. This ensures its suggestions are consistent with your existing codebase and project vision. As you build and make foundational changes, you should also have the AI help update this document, keeping it a living source of truth.

Best Practices for Day-to-Day Use

To get the most out of these tools and avoid common pitfalls, keep these principles in mind:

You are the developer, not the AI: The goal is assistance, not replacement. You still need to think critically, plan your work, and break down problems. Use the AI to execute the smaller, well-defined pieces faster.
Never copy-paste blindly: One of the strongest pieces of advice is to manually re-type code suggestions. This forces you to read and understand every line, helping you catch errors, learn new patterns, and maintain a sense of ownership. As one developer put it, blindly accepting code is how you create "legacy code at astonishing speed."
Practice good Git hygiene: Treat each interaction with the AI as an atomic change. Start from a clean commit, let the AI make its changes, and then carefully review the diff. This makes it trivial to accept, reject, or amend the changes and provides a clear history.
Master the art of the prompt: Quality over quantity is key. Provide focused context, giving the AI exactly what it needs and no more. Be explicit in your instructions, such as telling it not to change variable names or refactor unrelated code. If the AI starts generating poor results, don't try to correct it over and over; start a new session with a better prompt.