Most AI coding assistant tutorials jump straight to "write a prompt and watch the magic." That skips the setup decisions that determine whether the tool is actually useful for your codebase versus a novelty you stop opening after a week. Here's a more deliberate starting workflow.
Step 1: Pick a tool that matches where you already work
GitHub Copilot remains the most widely adopted tool in this category, with roughly 42% market share among paid tools and 1.8 million paying subscribers, largely on the strength of deep integration with VS Code, JetBrains, and Visual Studio, the lowest-friction starting point for most developers. Cursor has become the benchmark choice for developers who want more precise, multi-file control, with its Composer interface built specifically for complex refactors. Windsurf targets agentic-heavy workflows at a lower price point, with automatic project-wide indexing that becomes a real advantage on codebases with 500+ files. Start with the integrated option that fits your existing editor unless you have a specific reason to want a different environment.
Step 2: Configure scope before you configure prompts
Before writing a single prompt, check what the tool has access to: does it only see the current file, or can it index your whole repository for context? A common effective setup pairs a lower-cost tool like Copilot for daily autocomplete with a stronger reasoning tool like Claude Code for complex refactoring and architecture work. Using each where its actual strength lies, rather than expecting one tool to do both jobs well.
Step 3: Start with read-only, low-stakes tasks
Resist the urge to point a new tool at your hardest unsolved bug on day one. Start with tasks where a wrong answer is easy to catch and cheap to discard: writing unit tests for existing, well-understood code; generating documentation for a function whose behavior you already understand; refactoring a small, well-tested piece of code. This builds an accurate sense of the tool's actual reliability on your specific codebase before you trust it with anything higher-stakes.
Step 4: Write prompts that reference your actual code, not abstractions
"Write a function that validates user input" produces generic output. "Write a function that validates the `SignupForm` fields defined in `models/forms.py`, following the same validation pattern used in `LoginForm` in the same file" produces output that actually fits your codebase, because it gives the model concrete anchors instead of asking it to guess your conventions. This is the same specificity principle covered in our guide to writing better AI prompts, applied to code.
Step 5: Take the productivity claims with real skepticism, and review every suggestion
This is the step most commonly skipped, and it matters more than the marketing around these tools suggests. METR's early-2025 randomized controlled trial on experienced open-source developers found that AI coding assistance actually made tasks take 19% longer on average, a genuinely counterintuitive result, covered in more depth in our piece on AI copilot productivity, that should reset expectations about guaranteed speedups. Treat every AI-generated suggestion as a first draft requiring the same review discipline you'd apply to a human collaborator's code. Checking for subtle logic errors, inefficient approaches, or security issues like a hardcoded credential or missing permission check, not as a finished product. Our guide to fact-checking AI-generated content covers the same underlying discipline applied to text rather than code.
What to expect after the first week
Set expectations based on task type, not a blanket productivity multiplier: boilerplate, tests, and well-scoped refactors tend to see genuine speedups quickly; deep debugging in unfamiliar, poorly documented code may not, and per METR's findings, can even slow experienced developers down. That's a more realistic starting expectation than either dismissing the tool after one bad suggestion or over-trusting it after a few good ones.