Last updated: July 2026 · Covers Cursor, Cline, Aider, Claude Code, and Windsurf
A well-configured system prompt and a well-written rules file only get you so far. The actual instruction you type for a specific task — "fix the login bug," versus a precise description of the bug, its reproduction steps, and what a correct fix looks like — is often the single biggest factor in whether an AI coding agent produces something useful on the first try.
This guide is about that individual task prompt: the message you write fresh every time, not the persistent configuration covered in the System Prompts for AI Coding Agents guide. The two are complementary, and the distinction matters.
Three different things get called "prompting," and confusing them is the source of a lot of frustration with AI coding tools.
Rules files (.cursorrules, .clinerules, CONVENTIONS.md) persist across every session and encode project-wide conventions — naming, structure, testing approach. You write these once per project.
System prompts persist across every session too, but they encode agent behavior — decision-making defaults, universal constraints, communication style. Also written once, but focused on how the agent operates rather than how code should look.
Task prompts are what you type into chat for a specific request, right now, for this one task. They exist for a single interaction and disappear once the task is done. This guide is entirely about this third category — the thing you write every single time you ask an agent to do something.
A useful mental model: rules and system prompts are the agent's standing instructions. The task prompt is the actual request. Even a perfectly configured agent produces mediocre output from a vague task prompt, and even an agent with no configuration at all can produce excellent output from a precise one — though the combination of both is obviously strongest.
Four components consistently separate task prompts that work on the first attempt from ones that require three rounds of correction.
"Fix the bug" is not a goal — it's a category. "Users are seeing a 500 error when submitting the signup form with an email that contains a plus sign" is a goal. The difference is specificity about the actual symptom, not just the area of the codebase.
For feature requests, the same principle applies: "add pagination" is a category; "add cursor-based pagination to the /api/orders endpoint, returning 20 results per page with a next_cursor field in the response" is a goal.
Agents with codebase indexing (Cursor, Windsurf) can find a lot on their own. But context that lives outside the code — a business rule, a decision made in a meeting, a constraint from a client — has to come from you, every time, because it is not written down anywhere the agent can read it.
"This needs to remain backward-compatible with the mobile app's v1 API, which we cannot update until Q3" is the kind of context an agent has no way to discover independently, and omitting it produces a technically correct but practically wrong solution.
State what should change and, just as importantly, what should not. "Update the calculateTax function to support the new EU VAT rates — do not modify the function signature, since it's called from 12 other places" prevents an agent from "helpfully" refactoring the signature and breaking every caller.
Scope boundaries are the single highest-leverage addition to a task prompt for any change touching shared or widely-used code.
For anything beyond a trivial change, state how you (or the agent) will know the task is done correctly. "This is done when the existing test suite passes and a new test confirms the edge case with a negative quantity is handled" gives the agent — and you — a concrete finish line, rather than an open-ended sense of "better."
For tasks touching more than two or three files, get the agent to outline its approach before it starts changing code:
Before making any changes, outline your plan: which files you'll
touch, what you'll change in each, and any assumptions you're
making. Wait for my confirmation before executing.
This costs one extra round-trip but catches misunderstandings — a wrong assumption about which function is the "right" one to modify, for instance — before they become a diff you have to unwind. Aider's /architect mode and Cline's Plan mode build this pattern into the tool directly; for tools without a dedicated planning mode (Cursor, Windsurf), asking for it explicitly in the prompt achieves the same effect.
"Follow the same pattern as UserService.createUser()" is more reliable than describing the pattern in prose, because it gives the agent a concrete, verifiable reference rather than asking it to reconstruct your mental model of "the way we do things here" from a description. This is especially valuable for codebases with conventions that are not written down anywhere (see Best AI Coding Tools for Legacy Codebases for more on inferring conventions from code alone).
"Refactor the entire authentication system to use JWT" invites the agent to make a large number of interconnected decisions in one pass, with no natural checkpoint for you to catch a wrong turn early. Breaking it into "first, add the JWT generation and verification utilities" → "now update the login endpoint to issue a JWT" → "now update the middleware to verify it" gives you a review point after each step and makes any single mistake much smaller and easier to isolate.
"There's a bug in checkout" gives the agent almost nothing to work with. "When a user applies a discount code to an order with only digital items, the tax calculation still includes shipping tax, which should be zero for digital-only orders" gives the agent an exact scenario to reason about and, ideally, to write a test against.
As covered in more depth in Best AI Coding Tools for Legacy Codebases, a strong task prompt pattern for uncertain code is: "First, write tests that capture this function's current behavior exactly as it is, including anything that looks like it might be a bug. Then, separately, fix the specific issue I'll describe." This gives you a safety net before any change is made.
"Improve," "clean up," "optimize," and "make better" are all vague verbs that leave the actual goal undefined. "Improve performance" could mean reducing database queries, reducing bundle size, reducing render time, or a dozen other things — and an agent will pick one, possibly not the one you meant. Replace with the specific outcome: "reduce the number of database queries in this endpoint from N+1 to a single query using a join."
"Add a cache" as a task prompt commits the agent to caching as the approach before establishing that caching is actually the right fix for whatever problem prompted the request. If you are not certain of the solution, describe the problem instead — "this endpoint is slow under load, investigate why and propose a fix" — and let the agent's investigation inform the approach, which it can only do if you have not already locked in a specific solution.
Failing to say "do not change the function signature" or "only touch files in src/components/checkout/" is how a task intended to be small becomes an agent "helpfully" touching a dozen files it inferred were related. This is not the agent malfunctioning — it is a genuine ambiguity in the prompt that the agent resolved in a direction you did not want.
In long sessions, context earlier in the conversation can get deprioritized as the conversation grows (see the context-window discussion in Cursor Rules Not Working for the mechanics of this). If a constraint mentioned three messages ago is still critical for the current task, restate it rather than assuming it is still weighted heavily.
As covered above, a single sprawling prompt for a complex task removes your ability to catch a wrong turn early. The cost of splitting a task into steps is a bit more of your time up front; the cost of not splitting it is discovering the wrong turn only after the whole thing is done.
Bug: [what the user experiences, in one sentence]
Reproduction: [exact steps or conditions that trigger it]
Expected behavior: [what should happen instead]
Suspected area: [file/function, if you have a hint — omit if you don't]
First, confirm you can identify the root cause and explain it back
to me before writing the fix. Then write a test that reproduces the
bug, followed by the fix.
Goal: [concrete outcome, not a category]
Context: [any business rule, constraint, or decision the agent
cannot infer from the code]
Scope: [which files/modules this should touch — and which it should not]
Done when: [how you'll verify this works]
Outline your approach first. Wait for confirmation before implementing.
Refactor: [what specifically — e.g., "extract the validation logic
in OrderService into a separate module"]
Constraint: [what must not change — e.g., "the public method
signatures must stay identical, since 8 other files call these"]
Reference pattern: [point to a similar existing structure in the
codebase, if one exists]
Show me the plan for which files change and how, before executing.
Write tests for: [specific function/module]
Cover: happy path, [specific edge cases you know about], and any
error conditions you can identify by reading the code.
Testing framework: [name it explicitly if the codebase's convention
isn't obvious from context]
If you find behavior that looks like it might be an unintentional
bug while writing these tests, flag it separately — don't fix it
as part of this task.
Explain what [file/function] does: its purpose, how it's called,
and what would break if its behavior changed.
Flag anything you're uncertain about rather than guessing.
Do not suggest changes — I want to understand this before deciding
whether anything needs to change.
Cursor and Windsurf benefit most from explicit scope statements, since their codebase indexing means they will readily pull in files you did not mention if the prompt leaves room for it — a strength for genuinely related changes, a liability when you want a narrowly scoped edit.
Cline responds well to task prompts that lean into its Plan/Act structure directly — describing the goal and then explicitly saying "show me your plan" reinforces the tool's native workflow rather than working against it.
Aider benefits from task prompts that reference specific files by path when working in a large repo, since its repository map is strong at finding structurally related code but a direct pointer is always more reliable than inference. See the Aider Rules guide for /add and /read commands that pair well with a precise task prompt.
Claude Code, with its large context window, tolerates longer and more detailed task prompts without losing coherence — a good fit for the more elaborate templates above on complex, multi-file tasks. See the Claude Code Rules guide for persisting some of this detail at the project level instead of retyping it each time.
For agents connected to external tools via MCP (databases, issue trackers, documentation — see How to Set Up MCP Servers), being explicit in the task prompt about when to use a connected tool ("check the database for the current schema before writing this migration") measurably increases the odds the agent invokes it, particularly early in a session before a pattern of use is established.
A system prompt is persistent configuration — the agent's behavior, decision-making defaults, and universal constraints — set once and applied to every interaction. A task prompt is what you type for one specific request, right now, and it does not persist. See System Prompts for AI Coding Agents for the full treatment of the persistent side of this distinction.
Usually because the task prompt left scope ambiguous, and the agent resolved that ambiguity in a direction you did not intend — often by "helpfully" touching files it inferred were related. Adding explicit scope boundaries ("only modify X," "do not change the signature of Y") is the most direct fix, more reliable than hoping the agent guesses your intended boundary correctly.
Not every task — for a one-line fix or a well-scoped single-file change, asking for a plan first just adds a round-trip with little benefit. For anything touching multiple files, anything on unfamiliar or legacy code, or anything where a wrong assumption would be costly to unwind, asking for a plan first is worth the extra step.
As long as it needs to be to include the goal, relevant context, scope boundaries, and success criteria — there is no fixed ideal length. A one-line fix might need three sentences. A complex feature with business rules the agent cannot infer might reasonably need a paragraph or two. The templates in this guide are a floor for what to include, not a target length to hit.
It matters less for very simple, unambiguous tasks, where a capable model can often infer intent correctly even from a vague prompt. It matters more, not less, as task complexity and ambiguity increase — a more capable model given an ambiguous prompt for a complex task will still make a plausible guess at what you meant, and a plausible guess is not the same as being right. Precision in the prompt reduces the odds of a plausible-but-wrong result regardless of model capability.
Yes, and it is worth doing for recurring task types — bug fixes, test generation, and similar patterns tend to repeat. Keep a small personal library of templates like the ones in this guide, adjusted for your team's specific conventions, rather than reconstructing the structure from scratch each time.
/add, /read, and /architect commands relevant to task prompt structure