prompt-engineer-toolkit

Prompt Engineer Toolkit - Production Prompt Engineering

Tier: POWERFUL Category: Engineering Tags: prompt engineering, chain-of-thought, few-shot, evaluation, testing, prompt versioning

Overview

Prompt Engineer Toolkit provides the complete lifecycle for production prompts: design patterns that work, testing frameworks that catch regressions, versioning systems that track changes, and evaluation rubrics that replace subjective "looks good" with measurable quality. This is not about clever tricks -- it is about treating prompts as production code with the same rigor.

Core Prompt Patterns

1. System Prompt Architecture

Every production prompt has a layered structure. Order matters.

┌──────────────────────────────────────┐
│  Layer 1: Identity & Role            │  Who the model is
│  "You are a senior code reviewer..." │
├──────────────────────────────────────┤
│  Layer 2: Capabilities & Constraints │  What it can and cannot do
│  "You can read files, run tests..."  │
├──────────────────────────────────────┤
│  Layer 3: Output Format              │  How to structure responses
│  "Always respond with JSON..."       │
├──────────────────────────────────────┤
│  Layer 4: Quality Standards          │  What good output looks like
│  "Include edge cases, cite sources"  │
├──────────────────────────────────────┤
│  Layer 5: Anti-Patterns              │  What to avoid
│  "Never fabricate citations..."      │
├──────────────────────────────────────┤
│  Layer 6: Examples                   │  Calibration via demonstration
│  "Here is an example..."            │
└──────────────────────────────────────┘

Layer Design Principles

| Layer | Principle | Common Mistake | |-------|-----------|----------------| | Identity | Be specific about expertise level | "You are an AI assistant" (too generic) | | Capabilities | Enumerate, don't imply | Assuming model knows available tools | | Output Format | Show exact schema | Describing format in prose instead of schema | | Quality Standards | Quantify when possible | "Be thorough" (unquantifiable) | | Anti-Patterns | State the actual failure mode | "Don't be wrong" (useless) | | Examples | Show edge cases, not just happy path | Only showing trivial examples |

2. Chain-of-Thought (CoT) Patterns

Standard CoT

Think through this step by step:
1. First, identify [what needs to be analyzed]
2. Then, evaluate [specific criteria]
3. Finally, synthesize [the conclusion]

Show your reasoning for each step.

When to use: Complex reasoning, math, multi-step logic When NOT to use: Simple classification, formatting tasks, creative writing

Structured CoT with Scratchpad

Use the following reasoning process:

<scratchpad>
- List relevant facts
- Identify applicable rules
- Work through the logic
- Check for edge cases
</scratchpad>

Then provide your final answer outside the scratchpad tags.

Advantage: Model can reason messy, output is clean.

Self-Consistency CoT

Solve this problem three different ways, then compare your answers.
If all three agree, that's your answer.
If they disagree, identify which approach is most reliable and explain why.

When to use: High-stakes decisions where correctness matters more than speed. Cost: 3x token usage. Use selectively.

3. Few-Shot Design

Shot Selection Criteria

| Criterion | Good Example | Bad Example | |-----------|-------------|-------------| | Representative | Covers typical input pattern | Only edge cases | | Diverse | Different input types/lengths | All same structure | | Edge-covering | Includes tricky cases | Only happy path | | Output-calibrating | Shows desired detail level | Overly verbose or terse | | Ordered | Simple → complex progression | Random order |

Few-Shot Template

Here are examples of the expected input and output:

Example 1 (simple case):
Input: [simple input]
Output: [simple output with annotation]

Example 2 (typical case):
Input: [typical input]
Output: [typical output with annotation]

Example 3 (edge case):
Input: [tricky input]
Output: [correct handling with annotation]

Now process this:
Input: {user_input}
Output:

Dynamic Few-Shot Selection

For production systems with thousands of examples:

1. Embed all examples
2. Embed the current input
3. Find K nearest examples by embedding similarity
4. Include those K examples as shots
5. Typical K: 3-5 (diminishing returns after 5)

4. Output Structuring Patterns

JSON Mode with Schema

Respond with a JSON object matching this exact schema:

{
  "analysis": {
    "summary": "string - one sentence summary",
    "severity": "string - one of: critical, high, medium, low",
    "findings": [
      {
        "issue": "string - description of the issue",
        "location": "string - file:line",
        "fix": "string - recommended fix",
        "confidence": "number - 0.0 to 1.0"
      }
    ],
    "overall_score": "number - 0 to 100"
  }
}

Rules:
- findings array must have at least one entry
- confidence must reflect actual certainty, not optimism
- overall_score: 90-100 (excellent), 70-89 (good), 50-69 (needs work), <50 (poor)

Structured Reasoning with Sections

Structure your response with these exact sections:

## Assessment
[1-2 sentence bottom line]

## Evidence
[Specific observations supporting the assessment]

## Risks
[What could go wrong, with likelihood estimates]

## Recommendation
[Specific actionable next steps with owners]

5. Prompt Decomposition

Complex prompts that try to do everything fail. Decompose them.

Single Responsibility Prompts

| Bad (monolithic) | Good (decomposed) | |-----------------|-------------------| | "Review this code for bugs, style, performance, security, and suggest improvements" | Prompt 1: "Identify bugs" / Prompt 2: "Check style" / Prompt 3: "Find performance issues" / Prompt 4: "Security audit" / Prompt 5: "Synthesize findings" |

Pipeline Pattern

Prompt 1 (Extract):    Input → structured data
Prompt 2 (Analyze):    Structured data → findings
Prompt 3 (Synthesize): Findings → recommendation
Prompt 4 (Format):     Recommendation → user-facing output

Each prompt is testable independently. A failure in Prompt 2 doesn't require re-running Prompt 1.

6. Calibration Techniques

Temperature Guidelines

| Task Type | Temperature | Rationale | |-----------|-------------|-----------| | Code generation | 0.0-0.2 | Correctness > creativity | | Classification | 0.0 | Deterministic expected | | Analysis/reasoning | 0.2-0.5 | Some flexibility in framing | | Creative writing | 0.7-1.0 | Diversity of expression | | Brainstorming | 0.8-1.2 | Maximum variety |

Confidence Calibration

For each finding, rate your confidence:

Confidence levels:
- VERIFIED: I can point to specific evidence in the provided context
- LIKELY: Strong inference from available information
- UNCERTAIN: Reasonable guess, but limited evidence
- SPECULATIVE: Possible but I'm reaching

Never state SPECULATIVE findings as VERIFIED.

Prompt Testing Framework

Test Case Design

Every production prompt needs a test suite.

Test Case Structure

{
  "test_id": "classify-urgent-001",
  "input": "Server is down, customers can't access the product",
  "expected": {
    "contains": ["critical", "immediate"],
    "not_contains": ["low priority", "can wait"],
    "format_regex": "^\\{.*\\}$",
    "max_tokens": 500,
    "required_fields": ["severity", "category"]
  },
  "tags": ["classification", "urgency", "happy-path"]
}

Test Suite Composition

| Category | % of Suite | Purpose | |----------|-----------|---------| | Happy path | 40% | Confirm basic functionality works | | Edge cases | 30% | Boundary conditions, unusual inputs | | Adversarial | 15% | Inputs designed to break the prompt | | Regression | 15% | Cases that previously failed |

Evaluation Rubric

Automated Scoring

| Dimens