A practical knowledge base for designing, testing, and improving prompts used in AI evaluation, technical QA, troubleshooting, documentation, software testing, and workflow optimization.
The material is based on hands-on use of generative AI tools and focuses on repeatable methods rather than collections of isolated prompts. The examples are model-agnostic and designed to be adapted to real tasks without exposing private or sensitive information.
Effective prompt engineering is a structured process: define the task, provide relevant context, set boundaries, specify the expected output, evaluate the result, and refine one variable at a time.
This repository provides:
- Practical prompt design principles
- Repeatable evaluation and testing workflows
- Troubleshooting and documentation methods
- Adaptable examples for common technical tasks
- Reusable templates for consistent prompt development
If you're new to this repository, I recommend exploring the content in this order:
- Prompt Design Principles
- AI Response Evaluation
- Technical Troubleshooting
- Software Testing & QA
- Practical Examples
- Reusable Templates
This repository is intended for:
- AI Evaluators
- Technical QA Testers
- Prompt Engineers
- Technical Writers
- AI Enthusiasts
- Anyone building reliable AI-assisted workflows
AI responses can sound confident while being incomplete, inconsistent, or incorrect. A well-designed prompt makes the objective and evaluation criteria explicit, which improves clarity, testability, and repeatability.
Prompt engineering does not replace verification. It creates a better process for identifying assumptions, checking evidence, reproducing results, and deciding whether an output is useful for its intended purpose.
- Define the outcome. State what a successful response must accomplish.
- Provide relevant context. Include only the information needed to perform the task.
- Set clear constraints. Identify scope, exclusions, safety requirements, and stopping conditions.
- Specify the output format. Request a structure that makes the response easy to review and reuse.
- Separate facts from assumptions. Ask the model to label uncertainty and missing information.
- Make verification explicit. Define how claims, commands, or results should be checked.
- Iterate methodically. Change one important variable at a time and compare the results.
- Protect sensitive data. Never include credentials, private keys, personal data, or confidential content.
See Prompt design principles for a detailed checklist.
- AI response evaluation: assess accuracy, instruction following, clarity, completeness, consistency, and uncertainty.
- Technical troubleshooting: move from symptoms and evidence to testable hypotheses and verified actions.
- Software testing and QA: generate test coverage, reproduce failures, and document expected versus actual behavior.
- Technical documentation: turn verified workflows into clear, audience-appropriate instructions.
The examples demonstrate how these methods can be applied, while the templates provide reusable starting points. The case studies show how to apply the methodology to fictional, anonymized scenarios.
For a companion handbook focused on response scoring, reviewer calibration, and evaluation consistency, see AI Response Evaluation Lab.
For complete worked evaluation reports across instruction following, reasoning, factuality, writing, technical, coding, safety, and multilingual tasks, see AI Evaluation Portfolio.
- Starting with a vague objective
- Providing large amounts of irrelevant context
- Combining unrelated tasks in one prompt
- Leading the model toward a preferred conclusion
- Treating fluent output as verified output
- Asking for claims without requesting evidence or uncertainty
- Changing several prompt variables at once
- Omitting expected behavior, environment details, or constraints
- Sharing secrets or private information in prompt context
- Reusing generated commands without reviewing their effects
- Begin with a simple baseline prompt before adding complexity.
- Define evaluation criteria before reviewing the answer.
- Use consistent test cases when comparing prompt versions or AI tools.
- Record the prompt, relevant context, result, and observed limitations.
- Distinguish expected behavior from actual behavior in technical testing.
- Include negative cases, edge cases, and recovery paths.
- Ask for clarification when required information is missing.
- Verify important claims with authoritative sources or direct testing.
- Keep reusable prompts modular and easy to update.
- Review every final output for privacy, safety, and factual accuracy.
- Practical prompt engineering guidance
- Repeatable evaluation workflows
- Technical troubleshooting methods
- Software testing patterns
- Documentation best practices
- Reusable templates
- Model-agnostic examples
ai-prompt-engineering/
|-- README.md
|-- CONTRIBUTING.md
|-- LICENSE
|-- .gitignore
|-- case-studies/
| |-- README.md
| |-- case-study-ai-evaluation.md
| |-- case-study-bug-analysis.md
| `-- case-study-documentation.md
|-- docs/
| |-- README.md
| |-- ai-response-evaluation.md
| |-- prompt-design-principles.md
| |-- software-testing-and-qa.md
| |-- technical-documentation.md
| `-- technical-troubleshooting.md
|-- examples/
| |-- README.md
| |-- documentation-review.md
| |-- response-evaluation.md
| |-- test-case-generation.md
| `-- troubleshooting.md
`-- templates/
|-- README.md
|-- bug-reproduction-template.md
|-- documentation-template.md
|-- response-evaluation-template.md
|-- structured-prompt-template.md
`-- workflow-test-template.md
- Add more sanitized, practical examples
- Expand evaluation criteria for different task types
- Add checklists for comparing prompt versions
- Document lightweight automation for repeatable evaluations
Suggestions, corrections, and improvements are welcome.
If you have ideas that improve prompt engineering, AI evaluation, documentation, or testing workflows, feel free to open an issue or submit a pull request.
See CONTRIBUTING.md for the content and review guidelines.
The examples in this repository are intended for educational purposes and demonstrate general prompt engineering practices.
Always validate AI-generated outputs before using them in production or high-stakes environments.
This project is available under the MIT License.