Prompt Engineering Patterns Advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. Core Capabilities 1. Few-Shot Learning Teach the model by showing examples instead of explaining rules. Include 2-5 input-output pairs that demonstrate the desired behavior. Use when you need consistent formatting, specific reasoning patterns, or handling of edge cases. More examples improve accuracy but consume tokens—balance based on task complexity. Example: Extract key information from support tickets: Input: "My login doesn't work and I keep getting error 403" Output: {"issue": "authentication", "error_code": "403", "priority": "high"} Input: "Feature request: add dark mode to settings" Output: {"issue": "feature_request", "error_code": null, "priority": "low"} Now process: "Can't upload files larger than 10MB, getting timeout" 2. Chain-of-Thought Prompting Request step-by-step reasoning before the final answer. Add "Let's think step by step" (zero-shot) or include example reasoning traces (few-shot). Use for complex problems requiring multi-step logic, mathematical reasoning, or when you need to verify the model's thought process. Improves accuracy on analytical tasks by 30-50%. Example: Analyze this bug report and determine root cause. Think step by step: 1. What is the expected behavior? 2. What is the actual behavior? 3. What changed recently that could cause this? 4. What components are involved? 5. What is the most likely root cause? Bug: "Users can't save drafts after the cache update deployed yesterday" 3. Prompt Optimization Systematically improve prompts through testing and refinement. Start simple, measure performance (accuracy, consistency, token usage), then iterate. Test on diverse inputs including edge cases. Use A/B testing to compare variations. Critical for production prompts where consistency and cost matter. Example: Version 1 (Simple): "Summarize this article" → Result: Inconsistent length, misses key points Version 2 (Add constraints): "Summarize in 3 bullet points" → Result: Better structure, but still misses nuance Version 3 (Add reasoning): "Identify the 3 main findings, then summarize each" → Result: Consistent, accurate, captures key information 4. Template Systems Build reusable prompt structures with variables, conditional sections, and modular components. Use for multi-turn conversations, role-based interactions, or when the same pattern applies to different inputs. Reduces duplication and ensures consistency across similar tasks. Example:

Reusable code review template

template

""" Review this {language} code for {focus_area}. Code: {code_block} Provide feedback on: {checklist} """

Usage

prompt

template

.

format

(

language

=

"Python"

,

focus_area

=

"security vulnerabilities"

,

code_block

=

user_code

,

checklist

=

"1. SQL injection\n2. XSS risks\n3. Authentication"

)

5. System Prompt Design

Set global behavior and constraints that persist across the conversation. Define the model's role, expertise level, output format, and safety guidelines. Use system prompts for stable instructions that shouldn't change turn-to-turn, freeing up user message tokens for variable content.

Example:

System: You are a senior backend engineer specializing in API design.

Rules:

-

Always consider scalability and performance

-

Suggest RESTful patterns by default

-

Flag security concerns immediately

-

Provide code examples in Python

-

Use early return pattern

Format responses as:

1.

Analysis

2.

Recommendation

3.

Code example

4.

Trade-offs

Key Patterns

Progressive Disclosure

Start with simple prompts, add complexity only when needed:

Level 1

Direct instruction

"Summarize this article"

Level 2

Add constraints

"Summarize this article in 3 bullet points, focusing on key findings"

Level 3

Add reasoning

"Read this article, identify the main findings, then summarize in 3 bullet points"

Level 4

Add examples

Include 2-3 example summaries with input-output pairs

Instruction Hierarchy

[System Context] → [Task Instruction] → [Examples] → [Input Data] → [Output Format]

Error Recovery

Build prompts that gracefully handle failures:

Include fallback instructions

Request confidence scores

Ask for alternative interpretations when uncertain

Specify how to indicate missing information

Best Practices

Be Specific

Vague prompts produce inconsistent results

Show, Don't Tell

Examples are more effective than descriptions

Test Extensively

Evaluate on diverse, representative inputs

Iterate Rapidly

Small changes can have large impacts

Monitor Performance

Track metrics in production

Version Control

Treat prompts as code with proper versioning

Document Intent

Explain why prompts are structured as they are

Common Pitfalls

Over-engineering

Starting with complex prompts before trying simple ones

Example pollution

Using examples that don't match the target task

Context overflow

Exceeding token limits with excessive examples

Ambiguous instructions

Leaving room for multiple interpretations

Ignoring edge cases

Not testing on unusual or boundary inputs When to Use This skill is applicable to execute the workflow or actions described in the overview.