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Your AI coding assistant usually changes the wrong files because it is being asked to make several decisions at once: infer the goal, discover the relevant context, choose acceptable constraints, and decide when the work is finished. Make those decisions explicit. Give it one outcome, name the files or symbols it may touch, provide the errors and tests that define the problem, state project rules and exclusions, and define a verifiable stopping point. Work in the interaction mode that matches the task, then review the diff and run the checks yourself. This turns an improvisational conversation into a small, auditable change request.
Why does my AI coding assistant keep changing the wrong files?
The assistant sees a text-and-tool environment, not the intent in your head. A request such as “fix authentication” leaves unanswered which failure matters, which implementation is authoritative, whether database changes are allowed, and how success will be measured. The model fills those gaps with guesses. A plausible guess can still produce a technically tidy change in the wrong module.
Most “fighting” is therefore a workflow problem rather than a personality problem. Scope drift, repeated retries, and unnecessary rewrites are symptoms of missing decisions. GitHub’s guidance for Copilot makes the responsibility explicit: “Remember that you are in charge, and Copilot is a powerful tool at your service.” You remain the person who defines the job and accepts the result.
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- Unbounded goal: a broad verb such as “improve,” “clean up,” or “modernize” invites unrelated work.
- Missing evidence: without paths, logs, current behavior, and tests, the assistant must search and infer.
- Unstated constraints: framework versions, API contracts, security rules, and style conventions may not be discoverable from the immediate file.
- No stopping rule: an agent can keep refactoring after the defect is fixed because nothing says what “done” means.
- Polluted history: old instructions from another feature can bias a new request.
The cure is to make the assistant’s decisions inspectable before it edits code. Ask for a small plan, constrain the surface area, and require objective checks.
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How do I bound an AI coding task so it cannot wander?
State one observable outcome, the permitted files or symbols, and explicit exclusions. Complex work should be split into separate requests instead of asking for discovery, architecture, implementation, and cleanup in one turn.
Define one outcome
Describe the behavior a user or test should observe. “Return a 400 response when the email field is missing” is bounded; “make validation better” is not. If there are several outcomes, create a sequence of tasks with a review point between them.
Name the change surface
List the repository paths, classes, functions, or configuration keys that are in scope. Tell the assistant whether it may add a test beside the implementation. A path list is more reliable than “the relevant files,” because it removes a search decision.
Write exclusions
Say what must not change: public API shape, database schema, dependency versions, generated files, formatting outside the target, or unrelated failing tests. Exclusions prevent a helpful-looking cleanup from becoming an unreviewable rewrite.
Example of a bounded request
Goal: Return HTTP 400 when POST /users receives no email, while preserving the existing response shape for all other validation errors.
In scope: src/users/createUser.ts and test/users/createUser.test.ts.
Out of scope: database migrations, route names, dependency updates, and formatting changes in other files.This wording gives the assistant a destination and a fence. It also gives you a simple way to reject a diff that crosses the fence.
What context should I give an AI assistant working in a codebase?
Provide the smallest set of facts needed to explain the current behavior and verify the fix: exact paths, relevant symbols, logs, failing tests, dependencies, examples, and the command that reproduces the problem. Context beats verbosity; a long general description is less useful than a short pointer to authoritative files.
Identify the repository landmarks
Point to the implementation, its callers, the nearest tests, and the configuration that controls it. If a value comes from an environment variable or generated client, name that source rather than pasting a large directory listing. Tell the assistant which file is canonical when similar implementations exist.
Show current behavior and expected behavior
Include a minimal input, the actual output or error, and the expected result. For a UI defect, describe the action, visible state, and browser or platform. For an API defect, include the method, path, status, representative payload, and relevant response body. Redact credentials, tokens, personal data, and production secrets before sharing logs.
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Include evidence that can be rerun
Give the exact test command, lint command, or reproduction command. Mention whether the failure is deterministic and the runtime or package-manager version when it affects the result. If a test is flaky or a service is unavailable, say so; otherwise the assistant may treat an environmental failure as a code failure.
Point to examples and contracts
Reference an existing function or test that demonstrates the project’s preferred pattern. Include API schemas, type definitions, migration rules, accessibility requirements, or compatibility promises that the change must honor. Do not assume the model will infer these from a distant document.
How do constraints and a stopping condition prevent scope drift?
Constraints tell the assistant how to make the change; a stopping condition tells it when to stop. Put both in the initial request, not after the first unwanted edit.
State technical constraints
- Framework, language, runtime, and package-manager versions.
- Public interfaces, serialized formats, and backward-compatibility requirements.
- Repository conventions such as error handling, naming, test style, and formatting.
- Security rules, including input validation, authorization boundaries, secret handling, and logging restrictions.
- Dependency policy: whether a new package is forbidden, allowed only with approval, or unnecessary.
Define “done when” in observable terms
A useful stopping condition names the checks and the behavior they prove: “The new test fails before the change, passes after it, the targeted lint command is clean, and no other files are modified.” If a full test suite is too expensive, identify the required subset and explain what remains unverified.
Use an explicit stop clause
Tell the assistant to stop after the smallest compliant change, report unresolved failures, and ask before adding dependencies or refactoring unrelated code. GitHub reports that vague prompts lead to retries, scope drift, and extra token use; a clear stopping point limits that overreach.
Which interaction mode should I use: inline completion, chat, plan, or agent?
Choose the mode according to the size of the decision, not personal preference. Local boilerplate benefits from a short completion; multi-file changes need a plan and controlled execution.
| Mode | Best use | What to provide | Main risk |
|---|---|---|---|
| Inline completion | Small, local boilerplate or a continuation inside a known function | Nearby types, a precise comment, and the intended return value | Accepting code without checking hidden assumptions |
| Chat | Explanation, diagnosis, or an iterative edit in a small area | Paths, error output, current behavior, and a narrow request | Conversation history can accumulate conflicting assumptions |
| Plan-first workflow | Architectural or cross-module work where sequencing matters | Goal, constraints, candidate files, risks, and approval to proceed | Planning can become a substitute for a bounded implementation |
| Agent mode | Multi-file implementation that requires search, edits, and test execution | Permitted tools, scope, commands, stop rule, and review checkpoints | Broad tool access can magnify a vague request |
For an architectural change, require a plan that lists the files to inspect and the smallest proposed change before granting edit or command permissions. For a one-line mapping or test fixture, inline completion is usually faster and easier to review.
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What should I put in an AI coding prompt for a codebase?
Use a repeatable structure so important decisions are not omitted under pressure. The following template is deliberately compact; replace each bracketed item with project-specific information.
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Goal: [single observable outcome].
Context: [repository paths, symbols, logs, failing tests, dependencies, examples, and current behavior].
Constraints: [architecture, versions, conventions, security rules, compatibility requirements, and scope exclusions].
Plan first: summarize the files you will inspect and the smallest change; wait for approval before editing.
Done when: [exact tests, commands, and observable behavior].
Stop: do not refactor unrelated code or add dependencies without asking.How to fill each field
- Goal contains one result, not a project slogan.
- Context points to authoritative evidence and distinguishes actual behavior from the desired behavior.
- Constraints captures rules the model cannot safely infer, including what it must leave alone.
- Plan first creates a review gate before edits and exposes a mistaken file assumption early.
- Done when connects completion to commands and a user-visible or test-visible result.
- Stop prevents “helpful” expansion and makes approval requirements explicit.
For a trivial local completion, you can omit the full template. For anything that touches more than one file, the structure pays for itself by making the proposed diff predictable.
How can I make an AI coding agent follow our project conventions?
Persist durable, project-specific guidance in the instruction mechanism your tool supports, and keep task-specific details in the prompt. Repository instructions should describe facts that remain true across work, not a generic lecture on programming.
Keep these durable rules
- Directory and module boundaries, including which layer owns a decision.
- Supported language, framework, runtime, and package-manager versions.
- Commands for formatting, linting, type checking, unit tests, integration tests, and builds.
- Preferred patterns for errors, logging, configuration, migrations, and public API changes.
- Security and privacy requirements, such as never logging tokens or bypassing authorization checks.
- Completion criteria and the files that must be included when behavior changes.
Remove guidance that causes collisions
Delete obsolete versions, contradictory style rules, broad “always refactor” instructions, and advice that applies only to a single feature. Generic rules consume attention without grounding the assistant in your repository. When a convention changes, update the durable instruction once rather than repeating a correction in every chat.
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A repository instruction can say “run the targeted test and lint command.” The task brief must say which test and which files apply today. Keeping those layers separate makes a new task easier to read and reduces the chance that an old feature’s assumptions leak into it.
When should I reset the assistant’s context?
Start a fresh session for an unrelated task or whenever earlier conversation history is producing wrong assumptions. A reset is often faster than repeatedly correcting a model that is anchored to a previous plan.
Signs the context is polluted
- The assistant keeps editing files you excluded after you restate the scope.
- It refers to a previous feature’s API, test command, or framework version.
- It proposes fixes for an old error that is not present in the current logs.
- Its plan changes after every correction instead of converging on the stated goal.
What to carry into the new session
Copy only the current task brief, the minimum repository paths, fresh evidence, constraints, and done criteria. Do not paste the entire old transcript. If the task is a continuation, summarize the accepted decisions and the exact remaining work in a few lines.
How do I know whether AI-generated code is safe to merge?
Treat generated code as a draft until a human has inspected the diff, checked assumptions and edge cases, and run the relevant validation. Passing tests are necessary evidence, not proof that the change is correct or safe.
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Read the diff before running a broad suite
Confirm that every changed file is in scope, the implementation matches the requested behavior, and no generated, lock, configuration, or migration file changed unexpectedly. Look for renamed symbols, altered defaults, widened permissions, and deleted error handling. A small diff that violates a contract is more dangerous than a larger but intentional one.
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Inspect assumptions and edge cases
Check empty, malformed, duplicate, unauthorized, timeout, and partial-failure inputs where they apply. Verify that user-controlled data is validated and escaped, secrets are not exposed, and authorization is enforced at the server boundary rather than only in the interface. Confirm that concurrency, retries, and idempotency behavior still match the system’s contract.
Run targeted and project-level checks
- Run the focused test that reproduces the original defect.
- Run the relevant lint, formatter check, and type checker.
- Run integration or end-to-end checks when the change crosses a boundary such as a database, queue, or HTTP API.
- Run the broader suite required by the repository’s contribution rules.
- Record any environmental or unrelated failures instead of claiming the task is complete.
GitHub’s best-practice guidance places the developer in charge; that principle matters most at merge time. You, not the assistant, own the security and compatibility decision.
Why does the assistant still touch the wrong files after I wrote a prompt?
Use the failure pattern to identify which decision is still ambiguous. Correct the missing input, then restart from a clean plan rather than arguing with a growing diff.
| Symptom | Likely missing decision | Recovery |
|---|---|---|
| It edits a similarly named module | The canonical path or symbol was not identified | Name the exact file, its caller, and the file that must remain untouched; ask for a file list before edits. |
| It rewrites working code | The requested outcome and exclusions were broad | State the failing case, preserve existing behavior elsewhere, and require the smallest diff. |
| It adds a package | Dependency policy was unstated | Forbid new dependencies unless the assistant explains the need and waits for approval. |
| It keeps retrying tests | There is no stop rule for environmental failures | Define the retry limit or required evidence and require a report of infrastructure failures. |
| It follows an old convention | Session history or instruction files conflict | Start a fresh session and remove obsolete repository guidance. |
| It changes API behavior while fixing internals | Compatibility requirements were implicit | State the public contract, add a regression test, and prohibit route, schema, or response-shape changes. |
Ask for a plan containing only the files to inspect and change. If that list is wrong, no code has been damaged yet; correct it before enabling edits.
How should I compare two AI coding assistants?
Compare workflow capabilities that affect control and review, not unsupported claims about speed or accuracy. Two assistants can produce different results simply because one has better repository grounding or a safer permission model.
| Capability | Questions to ask | Why it matters |
|---|---|---|
| Context access | Can it target files, symbols, tests, logs, and dependency metadata without loading irrelevant material? | Precise context reduces guessing and token waste. |
| Instruction support | Can the repository store project-specific rules, and are they applied consistently? | Durable conventions prevent repeated corrections. |
| Interaction modes | Does it offer inline, chat, plan, and agent workflows suited to different task sizes? | The right mode limits permissions and cognitive overhead. |
| Tool permissions | Can you restrict file edits, shell commands, network access, and destructive operations? | Permission boundaries contain mistakes. |
| Plan and diff visibility | Does it show intended files and a reviewable patch before or during execution? | Early inspection catches scope errors. |
| Validation | Can it run the project’s tests and report exact commands and failures? | Reproducible checks distinguish completion from confidence. |
| Session control | Can you reset, summarize, or compact context without carrying stale assumptions? | Clean context is essential for unrelated work. |
| Repository grounding | How reliably does it use your actual conventions, contracts, and examples? | Grounding matters more than a generic demonstration. |
Run the same bounded task with identical context and acceptance checks if you need a fair comparison. Do not turn one successful or failed interaction into a universal ranking.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does a controlled end-to-end workflow look like?
A dependable workflow has a review gate before edits and a second gate before merge. The sequence below works for a bug fix, a small feature, or an agent-managed multi-file change.
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- Prepare safe evidence. Collect the reproduction, relevant paths, tests, and dependency information; remove secrets and unrelated logs.
- Request a file-and-risk plan. Require the assistant to list files it will inspect, files it expects to change, assumptions, and validation commands.
- Check the plan. Correct paths, exclusions, compatibility assumptions, and missing tests before permitting edits.
- Allow the smallest implementation. Keep tool permissions limited to the agreed surface and require the assistant to stop when done criteria are met.
- Inspect the diff. Reject unrelated formatting, dependency, configuration, or generated-file changes.
- Run validation. Execute the focused reproduction, static checks, integration checks, and the broader suite required by the project.
- Review behavior and security. Exercise edge cases, authorization, data handling, failure paths, and compatibility with existing callers.
- Record unresolved issues. If a test is blocked by the environment or a requirement remains uncertain, document it and do not present the work as fully verified.
This process is intentionally human-controlled. The assistant accelerates search and implementation; you retain decisions about scope, risk, and acceptance.
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What if the task is architectural rather than a bug fix?
Increase the planning and review depth instead of making the prompt vague. Architectural work needs explicit alternatives, migration boundaries, and rollback thinking.
Ask for a decision record
Request the current architecture, constraints, proposed components, data flow, compatibility impact, risks, and rejected alternatives. Require references to the actual modules and contracts that support each claim. Do not approve implementation based only on a diagram or a persuasive summary.
Split migration from cleanup
Make one task establish the new path behind a safe boundary, another migrate callers, and a later task remove obsolete code after validation. Each stage should have its own tests and rollback condition. Combining migration, renaming, formatting, and dependency upgrades makes failures difficult to attribute.
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State when the assistant must stop and ask: an incompatible API is discovered, data loss is possible, a security control would be weakened, or the required test cannot be run. A no-go condition is as important as a success condition for high-impact changes.
What are the limits of prompt discipline?
Clear instructions reduce avoidable drift; they do not make generated code inherently correct. The assistant can misunderstand a contract, miss a runtime interaction, or produce a confident explanation for an unverified assumption. Keep credentials and sensitive production data out of prompts, use the least tool access that fits the task, and preserve normal code review and release controls.
Anthropic describes Claude Code as “intentionally low-level and unopinionated,” emphasizing flexibility and customization while acknowledging a learning curve. That trade-off applies broadly: flexibility is useful only when your workflow supplies the missing structure. The more authority an agent has to edit files or run commands, the more valuable explicit scope, permissions, plans, and review become.
Frequently Asked Questions
Should I paste the entire repository into an AI coding chat?
No. Point to the authoritative paths, symbols, tests, and configuration, then add only the logs and examples needed to explain the failure. Targeted context is easier to verify and lowers the chance that an irrelevant file steers the solution.
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Redact tokens, credentials, personal data, and production-only identifiers from logs before sharing them. Ask the assistant to use existing secret-management interfaces, and inspect the diff and logs for accidental disclosure before running or committing the change.
Is it safe to let an AI agent run shell commands?
Only with permissions appropriate to the task and a reviewable workspace. Restrict destructive operations, network access, and writes outside the agreed repository area, and require the agent to report the exact commands and results.
What should I do when tests fail for an unrelated reason?
Separate the environmental failure from the code result and capture the command, error, and affected component. Continue only with the checks that can run reliably, and leave the unresolved failure visible rather than marking the change fully verified.
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