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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Following emerging Large Language Model Operations (LLM Ops) best practices in the industry, you’ll learn all about the key technologies that enable Generative AI practitioners like you to leverage tools like LangChain, LLamaIndex, and more, to build complex LLM applications.
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<p align = "center" draggable=”false” ><img src="https://github.com/AI-Maker-Space/LLM-Dev-101/assets/37101144/d1343317-fa2f-41e1-8af1-1dbb18399719"
width="200px"
height="auto"/>
</p>
## <h1 align="center" id="heading">:wave: Welcome to LLM Ops: LLMs in Production, Cohort 1!</h1>
Access all of the concepts and code directly [YouTube](https://www.youtube.com/playlist?list=PLrSHiQgy4VjGQohoAmgX9VFH52psNOu71) to start building, shipping, and sharing with LLM Ops!
### Async Learning Outcomes
1. Build complex apps with LLM Ops frameworks including LangChain and LlamaIndex.
2. Understand LLM product development methods, from prompting to retrieval and beyond.
3. Build, deploy, evaluate, and operate your own end-to-end production RAG system.
*Note: This course was taught from August, 15, 2023 to September 7, 2023. As such, there may be aspects of the code that require updates for full functionality. Please submit pull requests directly if you find something that you can help improve!*
## LLM Ops Cohort 1, Session Videos
| Session | Video Link | Code | Slides |
| :-------- | :------------------------------------------------------------------------------------------------ | :-------- | :--------
| **Session 1: Course Intro, Building Your First RAG App with LangChain, Chainlit, and Hugging Face** <br />  | [🎥 Video](https://www.youtube.com/watch?v=d1Oj5vrTWC4&ab_channel=AIMakerspace) | [🐙 Repo](https://github.com/AI-Maker-Space/LLM-Ops-Cohort-1/tree/main/Week%201/Tuesday) | [💻 Slides](https://www.canva.com/design/DAFr0_xXRmM/J_F75d9OrhYgdQ6COF-jRw/edit?utm_content=DAFr0_xXRmM&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton) |
| **Session 2: Taking RAG to the Next Level with LangChain Agents** <br />  | [🎥 Video](https://www.youtube.com/watch?v=cwLFLlZzFfw&ab_channel=AIMakerspace) | [🐙 Repo](https://github.com/AI-Maker-Space/LLM-Ops-Cohort-1/tree/main/Week%201/Thursday) | [💻 Slides](https://www.canva.com/design/DAFr0_xXRmM/J_F75d9OrhYgdQ6COF-jRw/edit?utm_content=DAFr0_xXRmM&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton) |
| **Session 3: Building a More Robust RAG System with LlamaIndex Data Agents** <br />  | [🎥 Video](https://www.youtube.com/watch?v=n4GHCbQxLoY&ab_channel=AIMakerspace) | [🐙 Repo](https://github.com/AI-Maker-Space/LLM-Ops-Cohort-1/tree/main/Week%202/Tuesday) | [💻 Slides](https://www.canva.com/design/DAFsTCKwPGw/sxlXSvaU3C6HQCNQRftp9g/edit?utm_content=DAFsTCKwPGw&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton) |
| **Session 4: Project Ideation, Chainlit, and Fine-Tuning of an E2E RAG App with LlamaIndex** <br />  | [🎥 Video](https://www.youtube.com/watch?v=VHWfzkUZLh4&ab_channel=AIMakerspace) | [🐙 Repo](https://github.com/AI-Maker-Space/LLM-Ops-Cohort-1/tree/main/Week%202/Thursday) | [💻 Slides](https://www.canva.com/design/DAFse6cliLo/AR79LuNWJc-RX5IipCo_6g/edit?utm_content=DAFse6cliLo&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton) |
| **Session 5: Building a Production-Grade Open-Source RAG System with Llama 2, FastAPI, and Chainlit** <br />  | [🎥 Video](https://www.youtube.com/watch?v=Il2a0mzyJtI&ab_channel=AIMakerspace) | [🐙 Repo](https://github.com/AI-Maker-Space/LLM-Ops-Cohort-1/tree/main/Week%203/Tuesday) | [💻 Slides](https://www.canva.com/design/DAFs9I5CmQk/aglyznV--Vi4b-1iE5zO1A/edit?utm_content=DAFs9I5CmQk&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton) |
| **Session 6: Scalable Llama 2 Endpoints for RAG, Evaluation with RAGAS and Eluether AI Harness** <br /> ![](https:Excerpt of 4,899 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1bda6df68e8066a9, llm:Repository description and README: course materials teaching LLM Ops, building RAG systems, LangChain, LlamaIndex, agents, deployment, evaluation; includes code notebooks and session repos linked.
matched fp:1bda6df68e8066a9, llm:Repository description and README: course materials teaching LLM Ops, building RAG systems, LangChain, LlamaIndex, agents, deployment, evaluation; includes code notebooks and session repos linked.
matched fp:1bda6df68e8066a9, llm:Repository description and README: course materials teaching LLM Ops, building RAG systems, LangChain, LlamaIndex, agents, deployment, evaluation; includes code notebooks and session repos linked.
matched fp:1bda6df68e8066a9, llm:Repository description and README: course materials teaching LLM Ops, building RAG systems, LangChain, LlamaIndex, agents, deployment, evaluation; includes code notebooks and session repos linked.