Prompt_Engineering
This repository provides a comprehensive and hands-on guide to prompt engineering with 22 techniques, ranging from basic to advanced strategies for interacting with large language models (LLMs). Each technique is implemented in a self-contained Jupyter Notebook, complete with explanations, code examples, and practical demonstrations. The techniques cover a wide spectrum: fundamental methods like zero-shot, few-shot, and chain-of-thought prompting; advanced approaches such as self-consistency, generated knowledge, and directional stimulus; and applied strategies including prompt chaining, retrieval augmented generation (RAG), and structured output formatting. The notebooks are designed to be executable, allowing users to run and modify code against real LLM APIs (e.g., OpenAI) to see immediate effects of different prompt designs. The project is actively maintained, with regular updates to keep code compatible with the latest library versions and API changes. It serves as both a learning resource for beginners and a reference for experienced practitioners seeking to refine their prompting skills.
- Category
- LLM
- Pricing
- Open Source
Problem solved
Many developers, data scientists, and engineers struggle to consistently elicit accurate, contextually relevant, and well-structured responses from large language models. The repository addresses this by providing structured, tried-and-tested prompt engineering patterns. Users can learn how to craft prompts that reduce hallucinations, improve reasoning, handle complex tasks, and adapt to specific use cases, thereby boosting the performance and reliability of LLM-powered applications without needing to fine-tune models.
Use cases
- Learning and mastering prompt engineering through step-by-step executable tutorials
- Improving accuracy and consistency of AI chatbot responses for customer support
- Enhancing reasoning capabilities of LLMs for complex problem-solving tasks like math or logic
- Generating structured JSON outputs for programmatic consumption in applications
- Implementing retrieval-augmented generation (RAG) patterns to ground answers in external knowledge bases
- Experimenting with different prompting techniques to compare effectiveness for a specific domain
Audience
- Developers
- ML Engineers
- Data Scientists
Pros
- Comprehensive coverage of 22 prompt engineering techniques from basic to advanced
- Hands-on, executable Jupyter Notebooks with clear explanations and code examples
- Actively maintained with updates for library and API compatibility
- Open source and free to use, allowing customization and learning at one's own pace
- Covers a wide range of practical strategies including RAG, chain-of-thought, and few-shot learning
Cons
- Requires familiarity with Python and Jupyter Notebooks to fully utilize
- Depends on external API access (e.g., OpenAI) which may incur costs or require setup
- Limited to the 22 techniques covered; may not include the very latest research methods without community contributions
Official website: https://github.com/NirDiamant/Prompt_Engineering
Canonical LambdaBase page: https://www.lambdabase.com/tools/prompt-engineering