AI Prompt Engineering Best Practices

This guide covers proven techniques for creating effective AI prompts that consistently produce high-quality results across different AI models and use cases.

Core Principles

1. Clarity and Specificity

Use clear, unambiguous language
Be specific about desired outcomes
Define the scope and constraints
Avoid vague or open-ended instructions

2. Context and Role Setting

Establish the AI’s role and expertise level
Provide relevant background information
Set the appropriate tone and style
Define the target audience

3. Structure and Format

Use consistent formatting conventions
Break complex requests into steps
Specify desired output format
Include examples when helpful

Prompt Engineering Techniques

Role-Based Prompting

You are a [specific role] with [X years] of experience in [domain].
Your expertise includes [specific skills/knowledge areas].

Example:

You are a senior software architect with 15 years of experience in
distributed systems. Your expertise includes microservices design,
scalability patterns, and cloud architecture.

Chain of Thought Prompting

Break complex problems into logical steps:

Let's solve this step by step:
First, [step one]
Then, [step two]
Finally, [step three]

Few-Shot Learning

Provide examples of desired input/output pairs:

Here are some examples of the format I want:

Input: [example 1 input]
Output: [example 1 output]

Input: [example 2 input]
Output: [example 2 output]

Now please process: [your actual input]

Constraint Setting

Define clear boundaries and requirements:

Requirements:
- Length: Between X and Y words
- Format: Use bullet points
- Tone: Professional but conversational
- Audience: Technical stakeholders
- Constraints: No jargon, include examples

Advanced Techniques

Temperature and Creativity Control

Low temperature (0.1-0.3): For factual, consistent responses
Medium temperature (0.5-0.7): For balanced creativity and accuracy
High temperature (0.8-1.0): For creative, varied outputs

Context Window Management

Prioritize most important information early
Use summarization for large contexts
Structure information hierarchically
Remove redundant or outdated context

Start with a basic prompt
Test and evaluate results
Identify specific improvement areas
Refine and test again
Document successful patterns

Prompt Optimization Strategies

A/B Testing Prompts

Test different versions systematically:

Vary one element at a time
Test with consistent inputs
Measure specific success criteria
Document performance differences

Prompt Versioning

Keep track of prompt evolution:

Version 1.0: Basic prompt
Version 1.1: Added role definition
Version 1.2: Improved output format
Version 2.0: Complete restructure

Performance Metrics

Track these key indicators:

Accuracy: Correctness of outputs
Relevance: Alignment with requirements
Consistency: Reproducibility across runs
Efficiency: Time and tokens used
Usability: Ease of implementation

Common Pitfalls to Avoid

Ambiguous Instructions

❌ “Make this better” ✅ “Improve the code’s readability by adding comments and using descriptive variable names”

Overcomplicating

❌ Complex, multi-paragraph instructions ✅ Clear, concise, structured requests

Missing Context

❌ “Review this code” ✅ “Review this Python function for a web API that processes user authentication, focusing on security and performance”

Inconsistent Format

❌ Mixed formatting and unclear structure ✅ Consistent headers, bullet points, and examples

Testing and Validation

Test Cases

Create a suite of test inputs:

Edge cases: Boundary conditions
Typical cases: Common scenarios
Stress tests: Maximum complexity
Error cases: Invalid inputs

Evaluation Criteria

Define success metrics:

Functional: Does it work correctly?
Quality: Is the output high-quality?
Consistency: Same input = same output?
Efficiency: Optimal resource usage?

Feedback Loops

Establish improvement processes:

Collect user feedback regularly
Monitor output quality trends
Identify recurring issues
Update prompts based on learnings
Document best practices

Model-Specific Considerations

GPT Models

Respond well to detailed context
Benefit from explicit format instructions
Handle complex reasoning tasks
Good with creative and analytical prompts

Claude Models

Excel at following detailed instructions
Strong at maintaining character/role consistency
Good at structured outputs
Effective with safety-conscious prompts

Specialized Models

Code models: Focus on technical accuracy
Creative models: Emphasize style and tone
Analysis models: Provide detailed reasoning
Chat models: Maintain conversational flow

Documentation and Maintenance

Prompt Libraries

Organize prompts systematically:

Category: By use case or domain
Complexity: Basic, intermediate, advanced
Model: Optimized for specific models
Version: Track improvements over time

Best Practice Documentation

Document your learnings:

What works: Successful patterns
What doesn’t: Failed approaches
Context: When to use each technique
Examples: Real-world applications

Regular Reviews

Schedule prompt maintenance:

Monthly: Review performance metrics
Quarterly: Update based on new techniques
Annually: Complete prompt library audit
As needed: Address specific issues

Next Steps

Practice: Try these techniques with your prompts
Experiment: Test different approaches systematically
Measure: Track performance and improvements
Document: Keep records of what works
Share: Contribute successful patterns to the community

Remember: Effective prompt engineering is an iterative process. Start simple, test thoroughly, and refine based on results.

Using instruction files & VS Code agents

When you need repeatable, repository-scoped behavior (for example automated code review checks, consistent lint-like rules, or reusable workflows), prefer using instruction files or small custom VS Code agents instead of ad-hoc prompts.

Instruction files live in _instructions/ and should include concise frontmatter (title, applyTo globs) and short, imperative rules — keep them focused and example-driven.
Use instruction files for deterministic, machine-driven behaviors (e.g., a standardized code-review checklist) and prompts for exploratory or one-off generation tasks.
VS Code custom agents (defined as .agent.md files, often placed in .github/agents/) let you compose agent behavior and handoffs; prefer small single-purpose agents that reference instruction files rather than embedding long prompt text.

Quick mapping:

Use prompts when: quick prototyping, ad-hoc generation, or when you need highly interactive refinement.
Use instruction files when: you want consistent automated behaviors across PRs or CI, or when multiple contributors rely on the same guidance.
Use a custom VS Code agent when: you need an interactive persona (planner, reviewer, implementer) with specific tool access and optional handoffs between steps.