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LearnPrompt

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Basic Information

LearnPrompt is a free, open-source learning repository and course collection focused on AIGC (AI-generated content) and prompt engineering. The project provides organized tutorial content and case studies covering prompt engineering, ChatGPT, retrieval-augmented generation (RAG), AI agents, Midjourney and Runway workflows, Stable Diffusion text-to-image, digital humans, AI voice and music, and large model fine-tuning. The README notes a v4.0 update that adds a new UI, multi-language support, a comments area, daily selections, and a submission system. Documentation is available in Chinese and an English README is present. The repo also lists a roadmap of topics and references used to compile the materials. It targets learners and practitioners who want a structured, community-maintained curriculum and examples for exploring modern AIGC techniques.

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Stars
2157
App URL
https://github.com/LearnPrompt/LearnPrompt
Github Repository
https://github.com/LearnPrompt/LearnPrompt/blob/main/README.md

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App Details

Features
The repository bundles topic-based tutorials and curated case studies across many AIGC areas including prompt engineering, ChatGPT, RAG, agent design, image and video tools like Midjourney and Runway, Stable Diffusion, digital humans, and AI audio and music. Recent v4.0 enhancements include a redesigned UI, multi-language support, a comments module, daily curated articles, and a submission mechanism for contributions. The README shows a roadmap of completed and planned items, references to source material used in lessons, and a star history chart for project activity. Community contact options are provided via GitHub issues, an email address, and a study group; the project encourages feedback and contributions.
Use Cases
LearnPrompt helps learners and practitioners by offering a consolidated, continuously updated curriculum on AIGC topics and practical examples. It supplies curated daily selections to keep users aware of notable tutorials, a roadmap to track covered technologies, and references to original documentation that informed the lessons. Multi-language documentation and a comments area make the content more accessible and interactive. Community entry points and contribution instructions enable users to report issues, suggest edits, or add new materials. Overall the repo serves as a learning hub for those who want to study prompt engineering, experiment with multimodal tools, and follow practical guides for model usage and fine-tuning.

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