AI Coding Best Practices: Choose Among vs Code’s Five Modes

A compiler error with a useful message is a gift: it points to a mismatch, a missing import, or a value that isn’t what the code expects. An AI assistant can help trace that clue through unfamiliar files, explain a concept, suggest code, review a change, or take a repetitive bit of work off your hands. But when AI-generated code meets real users or new requirements, a product audit can help identify gaps in architecture, code quality, and UX. A plausible answer still isn’t the same as a correct one: you own the problem, the quality of the code, and the decision to use AI-generated code.

Good AI coding best practices aren’t about finding the magic prompt that makes every answer perfect. They’re about learning while you build: choosing a tool and interaction mode that fit the task, giving bounded requests and relevant context, managing agent work in reviewable steps, and testing and protecting the result. The interesting part isn’t just how much code an assistant can produce. It’s how well you can steer it, understand what changed, and decide what belongs in your project.

AI coding and vibe coding describe different scopes

AI-assisted coding broadly means using AI to generate, explain, review, debug, or improve code; vibe coding describes a narrower approach of using natural-language prompts to have AI generate code. Andrej Karpathy introduced the term “vibe coding” in February 2025, though people may use it differently.

Generating code from a description is only one way to work with an assistant. You might instead ask it to explain a function, help investigate an error, or suggest a review checklist. In each case, treat the answer as something to examine, not a verdict.

Large language models predict tokens based on patterns learned from text and code. They aren’t databases of verified answers or compilers with runtime awareness. Pattern-based output can make useful scaffolding, but it can also be wrong, outdated, or built around an API that doesn’t exist or has been deprecated. Check unfamiliar API names and behavior against the official documentation, then test the code in the project where it has to work.

Use AI to learn without giving up the problem-solving

A useful learning loop is to try a problem or sketch an approach, ask AI to explain or critique it, inspect the answer, and then try to recreate the solution yourself. That’s a practice, not a rule that you must get stuck alone before asking for help. A starting point simply gives the conversation something specific to work with.

AI can be especially handy for a concept that won’t click, an error that points into unfamiliar code, or a repetitive exercise that leaves little room to think about the underlying idea. The important move is to ask for the reasoning, not just the finished code. If you lean on generated answers every time, you may leave yourself less room to build programming skills. Reading, questioning, and trying the idea again keeps the problem-solving in your hands.

Think first and attempt the problem independently

Make a quick attempt or outline what you think should happen, then bring the snag to AI. You don’t need to finish the task unaided. Even a partial attempt can help you ask a more useful question than “solve this.”

Ask why instead of only how

Along with asking how to write a function, ask why a particular approach works, what concept it uses, or what tradeoff it makes. An explanation gives you something to inspect; it doesn’t prove the code is right.

Read the error message before asking AI to debug

First look at the diagnostic yourself. Then share the relevant error output and nearby code, ask what might explain it, and compare the suggested diagnosis with the code and documentation.

Practice your approach and ask AI to review it

If you’re stuck, show the approach you tried and ask where it might be going wrong or how it could improve. That invites feedback on your thinking without handing over the whole problem.

Understand and recreate AI-provided solutions

Trace what the generated code does and how its pieces fit together. Explain the logic in your own words, then try building the solution again without copying. That’s a useful check on what you understood, not proof that you’ve mastered the topic.

Ask for simpler explanations and basic examples

For a difficult concept, request a plain-language explanation and a small example that makes the moving parts visible. Check the explanation against the code or documentation, especially when details matter.

Supplement AI with human collaboration

Peers, Stack Overflow, course discussion boards, and professors can offer other ways to work through a problem. A course-specific question, in particular, may need context an AI assistant doesn’t have.

Use AI to brainstorm or improve mini-projects

Ask for ideas for a small project or feedback on one you’ve started. Keep the project work yours; an assistant can help you explore possibilities without guaranteeing a complete or original result.

Build a simple version of a useful or enjoyable application

Pick a small app you’d actually use, or just find fun to make, and build an approachable first version with AI guidance along the way. ChatGPT can explain code, give examples, and unpack how something works; Claude.ai is another conversational option for explanations and debugging, including with substantial text. Check either tool’s answers rather than treating a fluent explanation as authoritative.

Follow course policy and disclose AI contributions when required

Students should follow their institution’s rules and disclose AI assistance when those rules require it. AI can help with learning, debugging, or ideation, but it shouldn’t stand in for a student’s work.

For coursework at Champlain College, students should follow the Academic Honesty at Champlain College policy. Examples supplied for that context include the acknowledgement Portions of this code were assisted by ChatGPT (OpenAI, 2025). and the APA in-text citation (OpenAI, 2025). A supplied reference example is:

OpenAI. (2025). ChatGPT (4o mini) [Large language model]. https://chatgpt.com/

These are examples tied to the Champlain context, not universal formats or requirements. The rules for a particular course determine what to disclose and how.

Choose a coding assistant by workflow fit, not brand

Editor-integrated suggestions and conversational explanations serve different workflows, so neither GitHub Copilot nor a chatbot is universally better for learning to code or learning web development. Copilot can suggest code as you type and integrates with Visual Studio Code; its described capabilities also include chat, explanations, troubleshooting, command-line assistance, code review, pull-request summaries, and Copilot Spaces for sharing project information. The developer still reviews the result.

ChatGPT and Claude.ai are conversational examples for coding help: Claude.ai can handle substantial text, explain concepts, and help debug. Replit supports natural-language app generation, iterative feedback, debugging, and hints. A current, controlled product comparison or verified free-plan limits aren’t available here, so compare tools by workflow fit rather than assuming a particular feature is included.

Other examples include Cursor for codebase-wide edits and integrations with terminal, Slack, and GitHub; V0 for generating code, apps, agents, interface refinements, and prototypes; Claude Code for project reading, file edits, and tasks across files in several environments; and Amazon Q Developer for implementation, review, testing, documentation, refactoring, and updates. Codex, Gemini Code Assist, Windsurf, Codeium, TabNine, Google Colab, and Warp Code are also options, but the available details don’t support ranking them. Compare repository context, editing and testing, integrations, language and environment fit, usability, privacy, and price when considering whether web development is dead due to AI. Free features and terms can change.

Match the interaction mode to the task

In Visual Studio Code, let the task’s scope determine the interaction mode. A nearby completion doesn’t need an agent, and a multi-file change may need more than a suggestion in the editor; vibe coding is another way to describe directing AI with plain English. These five modes are VS Code examples, not a universal menu; use the least broad mode that fits what you’re trying to do.

• Inline suggestions fill in code in the flow of typing, such as a variable name or a familiar stretch of boilerplate. Treat the completion as a proposal, especially when it reaches beyond the line you’re working on.
• Inline chat is for a targeted edit in place. Ask it to refactor one function or add error handling to a specific block, then inspect the diff before keeping it.
• Plan is for structured preparation before implementation. Use it to think through an architecture change or migration strategy while the code is still untouched.
• Agents fit broader work across files, where planning and tool use are both needed. Their wider reach makes reviewable steps and developer oversight particularly important.
• Smart actions handle specialized, one-step tasks, such as drafting a commit message, fixing an error, or renaming a symbol. The short path is convenient, but it doesn’t make the change self-verifying.

The trick is choosing scope, not climbing a ladder toward maximum autonomy. If a function needs a small edit, a focused interaction keeps the change easy to inspect. If the task affects several files, a Plan step can help expose the intended direction before an agent starts changing things. Approve the outline first; then choose an agent task or smaller edits for implementation.

A task can also change shape as you learn more. Start with an inline suggestion, discover that the function’s assumptions are off, and switch to chat to review the approach. If the fix crosses several parts of the project, pause and plan before expanding the request. The mode should follow the actual work, not the urge to use the most elaborate tool in the editor.

Write bounded prompts with observable acceptance criteria

A useful coding prompt names the task, the relevant files and environment, the requested scope, and what counts as a working result. Replace “make this better” with a specific change someone can check.

Include only the details that apply:

• Task and scope: Name the file, function, or existing view to change, and say what should stay untouched.
• Environment and constraints: Give the language or framework and any relevant rules, such as “use no regular expressions.”
• Expected behavior: Specify inputs and outputs. For example, a TypeScript email validator should return true for user@example.com and false for invalid.
• Verification: Say what to test. For a token-bucket rate limiter, specify 10 requests per second, rejection of the 11th request, and refill after one second, then run the tests after implementation. Review and validate the AI’s output yourself.

These are examples, not production-ready specs. If requirements are unclear, tell the AI to ask clarifying questions before proceeding; clarification is better than guessing. Split larger requests into smaller steps and use focused follow-ups to correct course. For an existing results view, bound edits to that view and its tests, ask it to follow current component patterns, run the relevant tests, and summarize the behavior it verified.

Supply relevant project context without flooding the model

Useful context is the smallest set of project details that helps with the task, not a dump of the repository. In VS Code, references such as #<file>, #<folder>, and #<symbol> can point to relevant code where supported.

For a bug, that might mean the edited file, its imports and related types, a failing test, and the error or stack trace. Configuration such as package.json or tsconfig helps when the question depends on scripts, dependencies, or compiler settings. Terminal output, source-control changes, screenshots, Chrome browser previews, page elements, and console logs can show what happened at runtime. Use #fetch or a GitHub MCP tool for external pages or repository context where available.

More context isn’t automatically better. Irrelevant history can bury useful details or push them out of view. Start a fresh session for unrelated work, trim history, or use /compact or /fork where available. For large codebases, VS Code workspace indexing can use semantic search, language intelligence, and GitHub code search to support work across files. Multi-root workspaces and project instructions can mark boundaries across services or a monorepo, but indexing doesn’t guarantee complete repository understanding.

Start agent work with one bounded task

For a first agent task, use a trusted project and make unrelated changes easy to distinguish: commit or stash them before the agent edits. Choose one small task with an observable result, then provide the relevant files, constraints, errors, and test commands. If the implementation approach isn’t clear, talk it through first or ask for a Plan role.

A results-view change is a useful example: limit the work to the existing results view and its tests, use the project’s component patterns, run relevant tests, and request a summary of verified behavior. For a first interactive task in VS Code, the supplied recommendation is Chat view, Copilot session target, Agent role, and Manual permissions. That setup is specific to VS Code, not a requirement for other tools.

Pause at approval requests, inspect every changed file, and run the tests yourself. Commit only the changes you intended. Permission controls can limit what an agent does, but they don’t replace checking its work.

Plan complex agent work before implementation

Explore the repository, ask a Plan agent for an outline, and review or refine it before implementation begins on changes that cut across the codebase. Supply the requirements, current state, and constraints; screenshots, wireframes, or diagrams can help when the feature depends on them. Approve subtasks and dependencies before code changes start.

Then implement one approved piece at a time, checking it with tests or expected outputs where the task calls for them. Keep reviewing progress and the resulting changes or pull request.

Parallel work makes sense only when tasks are independent. For example, separate coding, review, and testing agents can work in isolated areas with clear boundaries, but they still need monitoring. Two or three agents is one suggested example for typical work, not a universal limit. Cloud sessions can work remotely and create pull requests for team review; assigning a GitHub issue is one possible workflow.

Configure project instructions and tools only for recurring needs

Customize a project’s AI setup when it repeatedly needs details the tool can’t infer reliably, such as architecture, conventions, environment setup, or external-system access. If those details aren’t a recurring problem, extra configuration may just become another thing to maintain.

Custom instructions can describe project standards, and /init can draft a starting point. AGENTS.md, scoped rules, custom agents, and skills can support recurring workflows such as test-driven development or security review; /create-agent and /create-skill are available creation options. Tools and Model Context Protocol (MCP) servers connect external systems, with configuration such as mcp.json where supported.

Keep instructions short and scoped, and enable only tools the project needs. search.exclude and files.exclude can omit noisy or generated files when appropriate. Generated instructions, agents, skills, and security reviews still need a human check.

Match the model and execution harness to the work

Think about two separate choices: the model’s reasoning needs and where the workflow’s tools should run. A faster model may be enough for a simple completion or boilerplate; planning, debugging, or architecture can call for a reasoning-oriented model. General models may suit explanations, while code-focused ones may suit code patterns, but that’s a useful distinction, not a rigid rule. The same prompt can produce different answers, so trying another model or adjusting reasoning effort may help.

The harness sets what the assistant can do in your project and where its tools run. Copilot, Claude, and Codex harnesses are described as available in VS Code sessions; local, background, and remote cloud sessions serve different needs. A model’s hosting location is separate from where the harness runs tools, so local tool execution doesn’t mean the model is hosted locally. BYOK, pinned model preferences, and automatic selection are options where available, with terms that depend on the environment.

More capable models and larger contexts can use more AI credits. Keep context focused and plan complex work before implementation, rather than spending on code you may discard.

Review agent approvals and changed files before committing

Check each approval request before granting it, then inspect every changed file. Treat the agent’s finish message as a handoff, not a substitute for review.

Run relevant tests yourself, even if the agent reports success. If the result misses the request, give focused feedback or return to a checkpoint where that makes sense. Commit only the intended changes after you’ve reviewed the diff and results. Permission settings can help control an agent’s actions, but they can’t tell you whether its edits belong in the project.

Test generated code and review what tests cannot establish

Yes, generated code can be insecure even when tests pass. Treat it as a starting point: build, compile, run, lint, type-check, and test relevant changes independently, including checks the assistant didn’t run.

• Exercise behavior: Try unusual inputs and edge cases. Property-based tests or assertions designed to catch altered behavior can help assess test quality where appropriate.
• Check unfamiliar APIs: Confirm names and usage in official documentation. A suggestion can rely on an API that’s nonexistent or deprecated.
• Review the design: Look for hidden assumptions, error handling, dependencies, architectural fit, maintainability, and changes beyond the request.
• Look for security and performance risks: Check for injection flaws, hardcoded secrets, missing input validation, and brittle or confusing patterns.

Passing tests tells you about the behavior those tests exercised. It doesn’t establish complete correctness, security, or architectural fit. AI can suggest possible defects or missing tests as a review aid, but check those findings against the code.

Protect secrets and check privacy, licensing, and policy

Don’t send company code or data until your organization’s rules and the service’s privacy protections permit it. Keep credentials, tokens, customer data, and other sensitive information out of prompts. For an example, replace a secret with [API_KEY] and use synthetic data such as user_1@example.com; redaction, environment variables, or secret-management tools may also help.

Before sharing proprietary code, check organizational policy and the selected service’s privacy terms. Enterprise or on-premises options may be relevant, but their availability doesn’t by itself establish that sharing is permitted. Check licensing for generated code and suggested dependencies, and follow applicable intellectual-property rules. For coursework, disclose AI assistance when the institution’s policy requires it.

Sanitizing a prompt helps, but it isn’t a way to retract sensitive information already sent to a service.

Use Git and recovery controls as separate layers

Git feature branches, selective staging, and a workspace checkpoint solve different problems. Start with a clean working directory or isolated branch, then check git status so you can see what was already changing before AI work begins.

Use a feature branch and review staged hunks

For example, make a branch such as feature/ai-refactor, inspect the status, and stage only intended changes with git add -p. Review the staged diff, test locally, and commit a focused change. Use git restore --staged to unstage selected work if needed. Restore, revert, and workspace checkpoints have different uses, so don’t reach for a destructive recovery command without checking what it affects.

Know what a workspace checkpoint cannot undo

A VS Code checkpoint can restore affected workspace files and chat history. It can’t reverse a completed terminal command, a deployment, or a change to an external service. Use Git for versioned code and the relevant service’s own recovery controls for effects outside the workspace.

Change approach when an agent is stuck

If an agent is going in circles, clear irrelevant context, simplify the request, provide a working example, or switch approaches instead of repeating the same prompt. One source suggests 15 minutes as a trigger to change course or start over. Treat that as a heuristic, not a universal timer.

Use AI for review aids and documentation, then verify them

AI can suggest possible defects, missing tests, or security and performance concerns; check each against the code. It can also draft documentation or a pull-request summary from a change summary. Compare the draft with the actual diff before relying on it.

Frequently Asked Questions