{"data":{"slug":"decodingai-magazine-llm-twin-course","name":"llm-twin-course","tagline":"Learn free end-to-end production LLM & RAG system with best practices","github_url":"https://github.com/decodingai-magazine/llm-twin-course","owner":"decodingai-magazine","repo":"llm-twin-course","owner_avatar_url":"https://avatars.githubusercontent.com/u/153360176?v=4","primary_language":"Python","stars":4383,"forks":732,"topics":["aws","bytewax","comet-ml","course","docker","generative-ai","infrastructure-as-code","large-language-models","llmops","machine-learning-engineering","ml-system-design","mlops","pulumi","qdrant","qwak","rag","superlinked"],"archived":false,"github_pushed_at":"2026-04-20T10:53:45+00:00","maintenance_label":"Slowing","stars_delta_30d":10,"url":"https://www.graphcanon.com/tools/decodingai-magazine-llm-twin-course","markdown_url":"https://www.graphcanon.com/tools/decodingai-magazine-llm-twin-course.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/decodingai-magazine-llm-twin-course","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=decodingai-magazine-llm-twin-course","description":"🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴","homepage_url":null,"license":"MIT","open_issues":8,"watchers":75,"ai_summary":"Provides a course for building an end-to-end production-ready Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) system, including source code and hands-on lessons.","readme_excerpt":"## 💰 Cost structure\n\nAll tools used throughout the course will stick to their free tier, except:\n\n- OpenAI's API, which will cost ~$1\n- AWS for fine-tuning and inference, which will cost < $10 depending on how much you play around with our scripts and your region.\n\n---\n\n## 🚀 Install & Usage\n\nTo understand how to **install and run the LLM Twin code end-to-end**, go to the [INSTALL_AND_USAGE](https://github.com/decodingml/llm-twin-course/blob/main/INSTALL_AND_USAGE.md) dedicated document.\n\n> [!NOTE]\n> Even though you can run everything solely using the [INSTALL_AND_USAGE](https://github.com/decodingml/llm-twin-course/blob/main/INSTALL_AND_USAGE.md) dedicated document, we recommend that you read the articles to understand the LLM Twin system and design choices fully.\n\n---\n\n## License\n\nThis course is an open-source project released under the MIT license. Thus, as long you distribute our LICENSE and acknowledge our work, you can safely clone or fork this project and use it as a source of inspiration for whatever you want (e.g., university projects, college degree projects, personal projects, etc.).\n\n----\n\n<table style=\"border-collapse: collapse; border: none;\">\n  <tr style=\"border: none;\">\n    <td width=\"20%\" style=\"border: none;\">\n      <a href=\"https://decodingml.substack.com/\" aria-label=\"Decoding ML\">\n        <img src=\"https://github.com/user-attachments/assets/f2f2f9c0-54b7-4ae3-bf8d-23a359c86982\" alt=\"Decoding ML Logo\" width=\"150\"/>\n      </a>\n    </td>\n    <td width=\"80%\" style=\"border: none;\">\n      <div>\n        <h2>📬 Stay Updated</h2>\n        <p><b><a href=\"https://decodingml.substack.com/\">Join Decoding ML</a></b> for proven content on designing, coding, and deploying production-grade AI systems with software engineering and MLOps best practices to help you ship AI applications. Every week, straight to your inbox.</p>\n      </div>\n    </td>\n  </tr>\n</table>\n\n<p align=\"center\">\n  <a href=\"https://decodingml.substack.com/\">\n    <img src=\"https://img.shields.io/static/v1?label&logo=substack&message=Subscribe%20Now&style=for-the-badge&color=black&scale=2\" alt=\"Subscribe Now\" height=\"40\">\n  </a>\n</p>","github_created_at":"2024-03-08T09:21:28+00:00","created_at":"2026-07-07T17:35:18.747791+00:00","updated_at":"2026-08-17T18:00:59.015118+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"},{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"aws","name":"aws"},{"slug":"bytewax","name":"bytewax"},{"slug":"comet-ml","name":"comet-ml"},{"slug":"docker","name":"docker"},{"slug":"infra-as-code","name":"infra-as-code"},{"slug":"large-language-models","name":"large language models"},{"slug":"llmops","name":"llmops"},{"slug":"machine-learning-engineering","name":"machine-learning-engineering"}],"trust":{"provenance":{"is_fork":false,"github_id":769066911,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-17T18:00:56.658Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":119,"last_release_at":null,"stars_delta_30d":10,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:05:07.975Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-17T18:00:57.890Z"},"deploy":{"source":"dockerfile:docker-compose.yml","self_host":true,"observed_at":"2026-08-17T18:00:57.890Z","managed_saas":false},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-17T18:00:57.890Z"},"has_docker":{"value":true,"source":"dockerfile:docker-compose.yml","observed_at":"2026-08-17T18:00:57.890Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-08-17T18:00:57.890Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When seeking an extensive guide with practical implementation for setting up LLM and RAG systems using industry best practices.","If you need to understand both the design choices and technical setup involved in deploying such systems on platforms like AWS."],"when_not_to_use":["Avoid if you're looking for cost-free development, as it requires use of paid APIs from services like OpenAI and AWS.","Not suitable if your primary goal is to learn theory only, as this repository emphasizes hands-on lessons over in-depth theoretical explanations."],"source":"enrich:decision_facts","observed_at":"2026-07-12T18:01:19.919Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Provides a comprehensive, free course on building production-ready LLM & RAG systems, including 12 hands-on lessons."}]}}