Stop Writing Bigger Prompts: Start Writing Better Task Contracts for AI Workflows

Stop Writing Bigger Prompts. Start Writing Better Task Contracts

Balraj Singh, a technical writer and software engineer, argues that most developers fail by writing longer prompts instead of better task contracts. His article, published June 25, 2026, on DEV Community, advocates treating AI prompts like code with explicit contracts that define success and scope.

Why This Matters

Most developers assume that adding more words to a prompt—roles, steps, examples—will improve AI reliability, but this approach doesn’t eliminate hidden decisions the model must guess. Balraj Singh argues that for serious software engineering, prompting should mirror explicit, testable contracts rather than clever wording; otherwise, technically valid answers become useless in real environments, wasting time and introducing risk from undefined success criteria.

Key Insights

• A prompt asks tasks; a task contract defines success with a goal, context, constraints, deliverable, and acceptance checks, reducing hidden decisions the AI must guess.
• Acceptance checks act as a lightweight test suite for the model’s output, verifying each recommendation maps to a stated requirement and every factual claim has a source.
• Short examples outperform vague role prompts; for instance, providing a sample ‘good finding’ with severity, evidence, and test teaches the model a concrete standard.
• Don’t solve every AI problem inside the prompt; use a placement guide: stable rules in system instructions, reusable procedures in templates, and changing facts in retrieval.
• Treat prompts like production code: keep representative cases, define what good looks like with format and evidence checks, and change one thing at a time for testable results.

Working Examples

Example of a task contract for code review instead of a vague prompt.

Goal:
Review this pull request for correctness and regression risk.
Context:
- This is a TypeScript service that processes subscription renewals.
- The diff changes retry handling after a payment timeout.
- Duplicate charges are the highest-risk failure.
Scope:
- Review the changed code and directly connected call paths.
- Do not comment on formatting or unrelated refactors.
Deliverable:
Return a table with:
1. severity,
2. file and line,
3. failure scenario,
4. evidence from the code,
5. smallest safe fix,
6. test that would catch it.
Acceptance checks:
- Do not report an issue without code evidence.
- Separate confirmed defects from possible risks.
- Say "not enough evidence" when the diff cannot support a conclusion.

A reusable template for constructing task contracts.

GOAL
What outcome should be produced?
CONTEXT
Which facts materially affect the answer?
CONSTRAINTS
What must the model do, avoid, or preserve?
DELIVERABLE
What exact form should the output take?
ACCEPTANCE CHECKS
How should the result be tested before it is returned?
UNCERTAINTY
What should the model do when evidence is missing?

Example of good versus bad findings in a code review task.

Good finding:
HIGH: retryPayment.ts:84
A timeout after the provider accepts payment can trigger a second charge.
Evidence: the retry path creates a new idempotency key.
Fix: reuse the original key until the operation reaches a terminal state.
Test: simulate provider success followed by a client-side timeout.
Bad finding:
"Improve error handling."
This is too broad and has no failure scenario or code evidence.

Practical Applications

• Use case: Code review for a TypeScript payment service; a task contract with scope and deliverable table reduces risk of duplicate charges. Pitfall: Vague prompts like ‘review this’ force the model to guess which issues matter, leading to irrelevant or risky feedback.
• Use case: Defining output schema for AI-generated test plans; acceptance checks ensure each test is tied to a failure scenario. Pitfall: Omitting acceptance checks allows output without evidence, hiding critical gaps in reasoning.
• Use case: Reusable templates for common workflows (e.g., API changes); contexts like ‘existing mobile client breakage risk’ anchor the model to real value. Pitfall: Mixing all three (rules, task, retrieval) into one giant prompt makes the system harder to update and debug.

References:

• https://dev.to/balrajola/stop-writing-bigger-prompts-start-writing-better-task-contracts-164d