Prompt Engineering Guide
A comprehensive, free resource for learning prompt engineering, context engineering, RAG, and AI agents, with guides, papers, notebooks, and courses.
Evidence shows this is a documentation/educational repository with no executable code or runtime behavior. Therefore, all trust-related criteria (least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, source attribution) are not addressed, scoring 0. There is no evidence of malicious behavior, but also no security mechanisms.
Self-consistency: README and package.json are consistent with no obvious contradictions, score 2. Dependency availability: package.json lists dependencies but no lock file or version ranges, score 1. Failure messages: no error handling or user-visible failure messages, score 0.
Audience and scenarios: README clearly targets researchers and developers with multiple scenarios, score 2. Capability boundaries: no explicit boundaries or limitations of the guide, score 1. Trigger precision: not applicable as there is no automation trigger, score 0. Environment fit: provides local run instructions but no compatibility notes, score 1.
Information architecture: clear directory structure, score 2. Install notes: provides local run steps, score 2. Naming stability: no version history but consistent naming, score 1. Examples and FAQ: extensive examples but no FAQ, score 2. Known limitations: not mentioned, score 0. License: MIT license clearly stated, score 2. Versioning and changelog: no version number or changelog, score 0. Maintenance responsibility: README welcomes PRs and issues but no explicit maintainer, score 1.
Output usability: guide content is well-organized and easy to use, score 2. Marginal value: provides a wealth of unique resources, score 2. Cost-benefit: free and open source but requires time to read, score 1.
Claim traceability: cites papers and external resources but without specific links, score 1. Cross-source corroboration: external media mentions but no links, score 1. Fact-inference separation: no clear distinction between facts and inferences, score 0.
- This is a documentation repository with no executable code, so runtime security or behavior cannot be assessed.
- Dependencies are not pinned, posing supply chain risks.
- No versioning or changelog, making updates hard to track.
What does this agent do, and when should you use it?
The Prompt Engineering Guide is a comprehensive open-source repository maintained by DAIR.AI, compiling guides, papers, lessons, notebooks, and resources for prompt engineering, context engineering, RAG, and AI agents. It caters to developers, researchers, and learners who want to understand and utilize large language models (LLMs). The guide provides a well-structured curriculum from basics to advanced techniques, covers applications like RAG and function calling, and includes risk and misuse discussions. It supports multiple languages, has a web version at promptingguide.ai, and offers supplementary DAIR.AI Academy courses, a lecture video, and Jupyter notebooks, aiming to enhance the understanding of LLM capabilities and limitations and to design effective prompts for tasks like question answering and arithmetic reasoning.
The repository primarily delivers structured learning content, including guide documents (Introduction, Techniques, Applications, Prompt Hub, Models, Risks, Papers, Tools, Notebooks), a curated paper list, and a 1-hour lecture video with slides and notebooks. It explains techniques such as zero-shot, few-shot, chain-of-thought, self-consistency, knowledge generation, prompt chaining, tree of thoughts, retrieval augmented generation (RAG), ART, APE, Active-Prompt, directional stimulus prompting, PAL, ReAct, multimodal CoT, and graph prompting. It also provides specific prompting guidance for models like ChatGPT, GPT-4, LLaMA, and Mixtral, and discusses adversarial prompting, factuality, and biases. For local execution, the project uses Nextra (Next.js-based) and can be built with Node.js >=18.0.0 and pnpm.
- A machine learning engineer wants to optimize LLM performance on question answering and reasoning tasks by learning advanced techniques like chain-of-thought and self-consistency. They can refer to the Techniques section.
- An AI product developer needs to design robust prompts to interface with LLMs and other tools. They can explore RAG, function calling, and prompt chaining applications.
- An educator or trainer is preparing a course on prompt engineering and can use the lecture video, slides, and notebooks as teaching material.
- A researcher wants to stay updated with the latest papers and trends in prompt engineering. They can browse the Papers section.
- A contributor wants to translate the guide into a new language. They can run the guide locally to check translations, as described in the README.
What are this agent's strengths and limitations?
- Comprehensive and systematic coverage, from basics to advanced techniques, with a rich collection of papers and tools for further study.
- Multi-language support (13 languages) and an active community (Discord, Twitter, YouTube) with course offerings to suit different learning levels.
- Includes lecture video and code notebooks, combining theory and practice for hands-on learning.
- Backed by sponsors and DAIR.AI brand, indicating stable maintenance and ecosystem support.
- It is a learning resource, not a deployable tool, so users need to apply the techniques themselves in their own projects.
- Local setup requires Node.js 18+ and pnpm, which might be a hurdle for those unfamiliar with frontend toolchains.
- New features and translations may lag behind the latest model developments; users should supplement with other sources.
- Advanced topics like RAG and multimodal CoT require additional background knowledge, potentially challenging for beginners.
How do you install or deploy this agent?
To run the guide locally, you need Node.js >=18.0.0 and pnpm installed. After cloning the repository, run 'pnpm install' to install dependencies (including next, react, react-dom, nextra, nextra-theme-docs). Then start the development server with 'pnpm dev'.
How do you use this agent?
After starting the local server, open http://localhost:3000/ in your browser to browse the guide. For the online version, visit https://www.promptingguide.ai/. To use the code examples, clone the repo and open the .ipynb files under notebooks/. For structured courses, visit the DAIR.AI Academy and use code PROMPTING20 for a 20% discount.