How to Use GitHub Copilot's Chat and Agent Features for Faster Development

Most teams that install GitHub Copilot get real value from day one, inline completions are genuinely useful, and then stop there. That's a little like buying a car and only ever using first gear. Copilot's completions were its original feature, but the tool has grown into something much bigger: an in-editor chat for answering questions without leaving your file, an agent mode that edits across multiple files and fixes its own errors, a cloud-based coding agent that can be assigned a GitHub issue and come back with a pull request, automated PR summaries and code review, and custom instructions files that give it lasting context about your specific project. Used together, these cut out a meaningful amount of the small, repetitive work that eats a developer's day, not just the typing.

For background on GitHub Copilot's pricing tiers and where it fits against a tool like Cursor, see our full GitHub Copilot profile.

Step 1: Use Copilot Chat Instead of Switching Tabs to Search

Copilot Chat lives in a side panel inside your editor, and the habit worth building first is reaching for it before you reach for a new browser tab. Asking "why is this function throwing a null reference here" or "what does this regex actually match" directly in Chat keeps the answer tied to the exact code you're looking at, with Copilot already aware of the surrounding file and often the broader project. This sounds like a small efficiency gain, but the real cost of switching to a search engine isn't the search itself, it's the context switch away from the problem you were solving, and every return trip costs a few seconds of re-orientation. Keeping that loop inside the editor adds up over a full day of work far more than it looks like it should from any single question.

Step 2: Learn the Slash Commands, Don't Just Type Full Sentences

Copilot Chat supports slash commands, shortcuts for the handful of requests you'll make constantly: /explain to get a plain-language walkthrough of a selected block of code, /fix to propose a correction for a highlighted error, /tests to generate test cases for a function, and /doc to generate documentation comments. These aren't meaningfully different from typing the equivalent sentence, Copilot understands both, but they're faster to type, easier to remember as a habit, and they tend to produce more consistently scoped answers because the command itself narrows what Copilot is trying to do. If you only adopt one habit from this guide, make it learning the three or four slash commands you'll use daily and actually using them instead of re-typing the same request from scratch each time.

Step 3: Reach for Agent Mode When a Task Touches More Than One File

Chat is built for questions and small, scoped edits. The moment a task genuinely spans multiple files, adding a new field to a database model, updating the API route that uses it, and updating the frontend component that displays it, agent mode is the better tool. Agent mode can edit across multiple files in your codebase, run terminal commands, observe what happens, and fix errors it introduced, iterating until the task is actually done rather than stopping after the first file. The practical skill here is recognizing which category a task falls into before you start: a question or single-file tweak belongs in Chat, a change that ripples across several files belongs in agent mode. Starting a multi-file task in Chat usually means manually copying Copilot's suggestions from file to file, which defeats the point.

Step 4: Hand Well-Defined, Larger Tasks to the Cloud Coding Agent

For a task that's bigger than a single editing session but still clearly scoped, a well-written bug report, a small feature with a clear spec, GitHub Copilot's cloud-based coding agent can be assigned the GitHub issue directly and will work independently in a sandboxed environment to produce a pull request. It operates with read-only access to your repository and can only push to its own restricted branches, so it's a genuinely safe thing to hand off even on a shared codebase. The judgment call is in what you assign it: a vague, under-specified issue produces a vague, under-specified pull request, while a precise issue with clear acceptance criteria tends to come back with something close to mergeable. Treat writing a good issue for the coding agent the same way you'd treat writing a good issue for a junior teammate, the clarity you put in is what you get back out.

Step 5: Let Copilot Draft Your PR Summaries and a First Pass of Review

Once a pull request is open, Copilot can generate a summary of what changed and why, which is a genuinely useful starting point even on a PR you wrote yourself, since describing your own change accurately after the fact is a different skill than writing the code in the first place. Copilot's agentic code review goes further, leaving automated comments directly on a pull request that flag likely issues, and those suggested fixes can be routed back to the coding agent to open a follow-up fix PR. The right way to use this is as a fast first pass, not a replacement for a human reviewer: it catches a meaningful share of the small, mechanical issues (an unhandled edge case, an inconsistent naming pattern, a missed null check) so a human reviewer can spend their limited attention on the things that actually require judgment, architecture, business logic, whether this is even the right approach, rather than re-deriving the same mechanical checks Copilot already ran.

Step 6: Write a Custom Instructions File So You Stop Repeating Yourself

If you find yourself typing the same context into Copilot Chat over and over, "we use Tailwind, not plain CSS," "always use our internal logger, not console.log," "this repo follows the repository pattern, don't query the database directly from a controller," that's a sign you need a custom instructions file, not more typing discipline. A repository-level (or organization-level) instructions file gives Copilot that context permanently, so it applies automatically to every chat response, every agent task, and every suggestion in that codebase, instead of depending on whether you remembered to mention it this time. Setting one up is genuinely one of the highest-leverage things a team can do with Copilot, because the cost is a single file written once and the benefit compounds across every developer and every session afterward.

Step 7: Know When to Trust the Output and When to Actually Verify It

The single most common way Copilot wastes a developer's time isn't a failure of the tool, it's miscalibrated trust in either direction. Blindly accepting a multi-file agent change without reading the diff is how a subtly wrong assumption, a renamed variable Copilot missed in one file, a changed function signature that broke a caller it didn't touch, ends up merged. Manually re-verifying every single inline completion for a well-known, boilerplate pattern wastes the exact time Copilot exists to save. The practical rule of thumb: trust completions and small, well-scoped Chat answers for routine, well-understood code, and slow down to actually read the diff, line by line, for anything agent mode or the coding agent produced that touches business logic, security, data handling, or more than two or three files. The riskier and more consequential the change, the more that trust should shift toward verification, regardless of how confident Copilot's output sounds.

Putting It Together

None of these features replace the others, they stack. A typical well-used day might look like reaching for Chat and a slash command to understand an unfamiliar function, switching to agent mode for a feature that touches three files, handing a clearly scoped bug off to the cloud coding agent while you work on something else, letting Copilot draft the PR summary and a first review pass when you're done, and relying on a custom instructions file the whole time so none of those steps need re-explaining your project's conventions from scratch. The teams getting the most out of GitHub Copilot in 2026 aren't the ones with the best prompts, they're the ones who've matched each feature to the kind of task it's actually built for, and who've calibrated exactly how much to trust what comes back.

If your team is deciding whether to stay inside an existing IDE with Copilot or move to a dedicated AI-native editor instead, our GitHub Copilot vs Cursor comparison covers that tradeoff in more detail.