Prompt Library Template: Role → Context → Task → Constraints → Format

Every time an engineer writes a prompt from scratch, they spend 15–30 minutes on something they already wrote last week. No structure, no versioning, no reuse. The problem isn’t task complexity — it’s the absence of a system.

A prompt library fixes this. Not a collection of “best prompts from Twitter,” but an engineering tool with a consistent structure, versioning, and a catalog. At its core sits a 5-component template: Role → Context → Task → Constraints → Format. Each component controls a specific aspect of model behavior.

Why structured prompts work better

An LLM processes a prompt as a single text block. The model doesn’t distinguish “instruction” from “context” at the architectural level. But structure helps it distribute attention correctly across parts of the input.

Practical evidence and the prompt engineering guidelines from Anthropic and OpenAI point the same way: prompts with explicit section separation produce more relevant, more accurate responses than monolithic text of the same length. The model gets clear signals about priorities — that’s the whole mechanism.

The five template components each solve a different problem:

┌──────────────────────────────────────────────┐
│              Prompt Template                  │
├──────────┬───────────────────────────────────┤
│  Role    │ Who answers (expertise, tone)     │
├──────────┼───────────────────────────────────┤
│  Context │ What is known (data, situation)   │
├──────────┼───────────────────────────────────┤
│  Task    │ What to do (specific action)      │
├──────────┼───────────────────────────────────┤
│ Constr.  │ What to avoid (limits, prohibit.) │
├──────────┼───────────────────────────────────┤
│  Format  │ How to present (output structure) │
└──────────┴───────────────────────────────────┘

Each component can be swapped independently. The same Task with a different Role produces fundamentally different results. The same Role + Task with a different Format suits different output consumers. That modularity is what turns a prompt from a one-off text into a reusable tool.

Component 1: Role — who answers

Role sets the expertise, perspective, and tone of the response. The model pulls from different “knowledge domains” depending on what role you assign.

Bad: “You are a helpful assistant” Good: “You are a senior backend engineer with 10 years of experience in distributed systems. Specialization: PostgreSQL, event-driven architecture, Go”

An effective role contains three elements:

1. Expertise level. “Junior,” “senior,” “principal” — the model calibrates the depth and detail of the response.
2. Area of specialization. Specific technologies, domains, methodologies. The narrower, the more precise.
3. Behavioral framing. “You prefer simple solutions over complex ones,” “You always point out potential scaling issues.”

## Role
Senior DevOps engineer. 8 years of experience with Kubernetes,
Terraform, AWS. You prioritize reliability over cost optimization.
You always mention monitoring implications of proposed changes.

Role acts as a filter. Ask the same database question as a “senior backend engineer” versus a “CTO of a 5-person startup” and you get different answers: one is a technical analysis, the other is a strategic recommendation.

Component 2: Context — what is known

Context gives the model information it doesn’t have in its training data: facts about the project, current system state, business constraints, prior decisions.

Three types of context:

Static context doesn’t change between calls. Project description, technology stack, architectural principles. It makes sense to move it to system prompts or CLAUDE.md files.

Dynamic context changes with each call. Current code, an error from logs, the result of a previous step. Injected through template variables.

Implicit context the model extracts from the prompt itself: language, phrasing style, technical vocabulary level. Harder to control, but it still shapes the response.

## Context
Project: e-commerce platform, 50k DAU, Python/FastAPI backend.
Database: PostgreSQL 15, ~200GB, hosted on AWS RDS.
Current problem: order creation endpoint p99 latency increased
from 120ms to 800ms after last deployment (commit abc123).
Related: we added a new inventory check that joins 3 tables.
No changes to infrastructure or database configuration.

One rule: context should contain only relevant information. A model with a 200,000-token context window will process 50,000 tokens of context, but quality degrades as you load it up. Context management is a discipline on its own, and the minimum necessary always beats the maximum possible.

Component 3: Task — what to do

Task is the core of the prompt. One specific action, stated as an imperative. Not “tell me about query optimization,” but “find the cause of the p99 latency regression and propose a fix.”

Hallmarks of a good Task:

• Atomicity. One task, one output. If the task requires “analyze AND write AND test,” it needs to be split.
• Verifiability. It’s possible to unambiguously determine whether the task was completed.
• Specificity. “Improve the code” — bad. “Replace the N+1 query in OrderService.get_orders() with a JOIN and eager loading” — good.

## Task
Analyze the slow query log below and identify the root cause
of the p99 latency increase. Propose a specific fix:
either an index, a query rewrite, or an application-level change.
Include the exact SQL for any database changes.

For complex tasks, Task can contain numbered steps. The model follows them in order, which cuts down on skipped stages:

## Task
1. Identify which of the 3 JOINs in the query is the bottleneck
2. Check if an index on inventory.product_id would help
3. If yes — provide CREATE INDEX statement
4. If no — propose an alternative (query rewrite or caching)
5. Estimate the expected latency improvement

Component 4: Constraints — what to avoid

Constraints define what’s in bounds. Without them, the model picks the “most probable” answer, which may have nothing to do with your actual project constraints.

Four categories of constraints:

Technical. “No ORM, only raw SQL,” “Python 3.9+ compatible,” “Solution must work without downtime.”

Business. “Infrastructure budget: $500/month,” “Don’t touch the legacy billing module — it’s under audit,” “Solution needed in 2 hours, not 2 weeks.”

Style. “No unexplained abbreviations,” “Go code without generics (support for 1.17),” “Comments in English.”

Prohibitions. Explicit “don’t do X.” These are more reliable than hoping the model uses its own judgment. “Don’t suggest migrating to a different database,” “Don’t change the API contract.”

## Constraints
- Solution must be backward-compatible (no API changes)
- No additional infrastructure (no Redis, no Elasticsearch)
- Must work with current PostgreSQL 15, no extensions
- Migration must be online (zero downtime)
- Do not suggest application-level caching as primary solution

Constraints are the most underrated component. Most “bad” model responses aren’t caused by a bad prompt — they’re caused by missing constraints. The model proposes the ideal solution in a vacuum, with no sense of what’s actually possible.

Component 5: Format — how to present

Format defines the structure of the output. Without it, the model picks its own — and that’s almost always “a wall of text with markdown headers.”

Format solves two problems:

1. For humans. A structured output is easier to read and review.
2. For code. If the output is parsed programmatically, the format must be strict (JSON, YAML, a specific markdown structure).

## Format
Respond with:
1. **Root cause** (1-2 sentences)
2. **Fix** — exact SQL or code change, ready to copy-paste
3. **Risk assessment** — what could go wrong, how to rollback
4. **Expected improvement** — estimated latency after fix
5. **Verification query** — SQL to confirm the fix worked

Do not include explanations of basic concepts.
Use code blocks for all SQL and code.

For programmatic parsing, Format specifies a JSON schema:

## Format
Respond with valid JSON matching this schema:
{
  "root_cause": "string",
  "fix_type": "index" | "query_rewrite" | "app_change",
  "sql": "string | null",
  "code_change": "string | null",
  "risk": "low" | "medium" | "high",
  "estimated_improvement_ms": "number"
}

The complete template in action

The assembled prompt looks like this:

## Role
Senior backend engineer specializing in PostgreSQL performance
optimization. 10 years of experience. You prefer minimal,
targeted fixes over large refactors.

## Context
E-commerce platform, 50k DAU. Python/FastAPI, PostgreSQL 15
on AWS RDS (db.r6g.xlarge). Order creation p99 jumped from
120ms to 800ms after deploy on 2026-03-25.

New code adds inventory check:
SELECT o.*, p.name, i.quantity
FROM orders o
JOIN products p ON o.product_id = p.id
JOIN inventory i ON i.product_id = p.id
JOIN warehouses w ON i.warehouse_id = w.id
WHERE o.user_id = $1 AND w.region = $2;

Tables: orders (12M rows), products (500k), inventory (2M),
warehouses (50).

## Task
1. Identify the bottleneck in this query
2. Propose the minimal fix (index, rewrite, or both)
3. Provide exact migration SQL
4. Estimate improvement

## Constraints
- No downtime — online migration only
- No new infrastructure
- PostgreSQL 15, no extensions
- Keep the same API response format

## Format
1. **Diagnosis** (2-3 sentences)
2. **Fix** (SQL, copy-paste ready)
3. **Migration plan** (step-by-step, with rollback)
4. **Expected result** (estimated p99 after fix)

Every component can be swapped out. If p99 spikes in a different endpoint tomorrow, only Context changes. If the answer is for a CTO, Role and Format change. Moving to MySQL? Constraints change. The template works like a set of interchangeable parts.

Examples for different tasks

Code review

## Role
Principal engineer, code reviewer. Focus: correctness,
maintainability, security. You catch subtle bugs.

## Context
TypeScript/React project. PR #247: new payment form component.
Team convention: Zod for validation, React Hook Form, no any types.
{{code_diff}}

## Task
Review this PR. Flag bugs, security issues, and deviations
from team conventions. Approve or request changes.

## Constraints
- Don't comment on style (Prettier handles it)
- Don't suggest rewrites of working code
- Focus on logic errors and security only

## Format
For each issue:
- **File:line** — severity (critical/warning/nit)
- Description (1 sentence)
- Suggested fix (code block)

End with: APPROVE / REQUEST CHANGES / BLOCK

Writing documentation

## Role
Technical writer. You write for developers who have 5 minutes
to understand a new API. No marketing language.

## Context
Internal REST API for user management. FastAPI, OpenAPI spec
attached. Audience: frontend developers on the same team.
{{openapi_spec}}

## Task
Write API documentation for the /users endpoints.
Include request/response examples for each endpoint.

## Constraints
- No introductory paragraphs ("Welcome to our API...")
- No explanation of REST concepts
- Every example must be a valid curl command
- Use realistic data, not "John Doe"

## Format
For each endpoint:
## METHOD /path
Brief description (1 sentence)
**Request:** curl example
**Response:** JSON example
**Errors:** table of error codes

Data analysis

## Role
Data analyst. SQL expert. You explain findings in terms
that product managers understand.

## Context
SaaS product, B2B. PostgreSQL analytics DB.
Tables: users, subscriptions, events, invoices.
Current MRR: $180k. Churn last month: 4.2%.
{{schema_ddl}}

## Task
Write SQL queries to identify the top 3 predictors of churn.
Analyze the last 6 months of data.

## Constraints
- Queries must run under 30 seconds on 10M events table
- Use only standard PostgreSQL (no extensions)
- No ML models — pure SQL analysis

## Format
For each predictor:
1. **Finding** (1 sentence, plain language)
2. **Evidence** (SQL query + expected output description)
3. **Recommendation** (what product team should do)

Generating tests

## Role
QA engineer specializing in unit and integration testing.
You write tests that catch real bugs, not tests for coverage.

## Context
Go service, payment processing. Function: ProcessRefund().
Handles partial refunds, full refunds, idempotency.
{{function_code}}

## Task
Write test cases for ProcessRefund(). Cover happy path,
edge cases, and error scenarios. Use table-driven tests.

## Constraints
- Standard library + testify only
- No mocks of external HTTP calls — use interfaces
- Each test must be independent (no shared state)
- Test names must describe the scenario, not the method

## Format
Go test file, ready to compile. Each test case
in a table-driven format with: name, input, expected output,
expected error.

Organizing the prompt library

A prompt library needs structure, just like a codebase. Without it, the collection turns into a dumping ground.

File structure

prompts/
├── engineering/
│   ├── code-review.md
│   ├── debug-performance.md
│   ├── write-tests.md
│   └── migration-plan.md
├── product/
│   ├── spec-review.md
│   ├── user-story.md
│   └── competitive-analysis.md
├── data/
│   ├── sql-analysis.md
│   ├── dashboard-design.md
│   └── anomaly-investigation.md
├── writing/
│   ├── api-docs.md
│   ├── adr.md
│   └── incident-report.md
└── _components/
    ├── roles/
    │   ├── senior-backend.md
    │   ├── principal-engineer.md
    │   └── data-analyst.md
    ├── constraints/
    │   ├── postgresql-only.md
    │   ├── zero-downtime.md
    │   └── no-new-deps.md
    └── formats/
        ├── json-response.md
        ├── pr-review.md
        └── step-by-step.md

The key element is the _components folder. It holds reusable blocks: roles, constraints, formats. You assemble a prompt from parts rather than write one from scratch each time.

Prompt metadata

Every prompt in the library gets a header with metadata:

---
id: eng-debug-perf-001
name: Debug Performance Regression
version: 2.1
category: engineering
models: [claude-sonnet-4-6, gpt-5.4]
variables: [error_log, query, table_schema]
author: team-backend
last_tested: 2026-03-15
success_rate: 87%
---

Metadata makes filtering, versioning, and tracking effectiveness possible. The variables field specifies what data gets substituted at call time. The models field records which models the prompt has actually been tested against.

Versioning

Prompts evolve. Versioning follows the same logic as code:

• Patch (2.0 → 2.1): wording fixes, constraint clarifications
• Minor (2.1 → 2.2): new sections, task changes, adding steps
• Major (2.0 → 3.0): model change, complete rethinking of the approach

Git stores the change history. If you’re using Langfuse or a similar platform for prompt management, prompts are versioned there too. Once the library outgrows a handful of files, it’s worth pairing it with a full prompt management system for deploy and monitoring.

Testing prompts

A prompt without tests is like a function without tests — it works until the first change.

Minimum set for each prompt:

1. Golden test. Fixed input → expected output. Verifies that the prompt performs its core task.
2. Edge case test. Empty input, overly long input, invalid data. Tests robustness.
3. Format test. Parsing the output. If Format specifies a JSON schema, the output must be valid JSON.
4. Regression test. When a prompt is updated, all previous tests are run.

def test_debug_prompt():
    response = call_llm(
        prompt=load_prompt("eng-debug-perf-001"),
        variables={
            "error_log": SAMPLE_ERROR_LOG,
            "query": SAMPLE_SLOW_QUERY,
            "table_schema": SAMPLE_SCHEMA,
        }
    )
    result = json.loads(response)
    assert "root_cause" in result
    assert result["fix_type"] in ["index", "query_rewrite", "app_change"]
    assert result["risk"] in ["low", "medium", "high"]

Anti-patterns of prompt libraries

The novel-length prompt. A 2,000-word prompt trying to cover every possible scenario. The model loses focus, quality drops. Fix: break it into atomic prompts, one per task.

Copy-paste inheritance. Ten prompts with the same Role duplicated by hand. When the role changes, all ten need updating. Fix: component architecture with _components/.

Missing Constraints. “Write code” with no limits. The model picks its own defaults, which may not match what you need. Fix: always define at least 3–5 constraints.

Rigid Format without reason. A JSON schema for output that only a human will read. The unnecessary constraint degrades content quality. Fix: JSON and strict formats only when the output is parsed programmatically.

Prompt rot. Prompts written for GPT-4o still running against Claude Opus 4.6 with no updates. Different models respond differently to structure, length, and instruction style. Fix: a models field in metadata and regular testing.

Adopting in a team

A prompt library only becomes valuable when the whole team uses it, not just one person.

Step 1: audit. Collect all prompts the team uses regularly. Usually 10–15 per team of 5.

Step 2: standardize. Rewrite each one using the 5-component template. Takes 15–20 minutes per prompt.

Step 3: catalog. Organize into a file structure, add metadata. Pull common components into _components/.

Step 4: integrate. Wire it into the workflow. Prompts from the library should be accessible in the IDE (via CLAUDE.md or .cursorrules), in CI/CD (via API calls), in Langfuse (via prompt management).

Step 5: review. New prompts go through review, just like code — checked for template structure, tests, and metadata.

A prompt library isn’t a PDF of “top 50 prompts.” It’s an engineering artifact with versioning, tests, and CI. Role → Context → Task → Constraints → Format — five components, each controlling a specific aspect of model behavior, each changeable independently. Five templates covering the team’s most frequent tasks are enough to see measurable improvement in both quality and speed.

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