Awesome AI Agents › Courses and Tutorials
The ragbook-notebooks repository is a comprehensive collection of Jupyter notebooks that accompany the "Building LLMs for Production" book by Towards AI. This repository serves as a practical resource for readers to deepen their understanding of large language models (LLMs) and their applications in production environments. The notebooks are organized by chapters, each focusing on different aspects of LLMs and related technologies. Starting from foundational concepts, the repository includes notebooks on transformer architectures and understanding transformers, which are essential for grasping the underlying mechanisms of LLMs. It then progresses to practical topics such as prompt engineering, building applications powered by LLMs using frameworks like LangChain, and creating summarizers for news articles. Further chapters delve into advanced techniques including the use of prompt templates, few-shot learning, output management, and knowledge graph creation from textual data. The repository also covers important tools and concepts like text splitters, chains, and guarding against undesirable outputs using self-critique chains. Advanced retrieval-augmented generation (RAG) techniques are explored, along with metrics and evaluation methods, and introductions to tools like LangSmith. The later chapters focus on building agents for tasks such as analysis report creation, database querying, and multimodal finance applications, leveraging LlamaIndex and OpenAI assistants. The repository also provides notebooks on fine-tuning LLMs for specific domains such as financial sentiment and medical data, using various methods including QLoRA and reinforcement learning with human feedback (RLHF). Performance benchmarking for inference is also included. Overall, ragbook-notebooks is a valuable educational and practical resource for anyone interested in building, fine-tuning, and deploying large language models in real-world applications, offering hands-on examples and detailed explorations of state-of-the-art techniques.
https://github.com/towardsai/ragbook-notebooks
A comprehensive guide and resource hub for prompt engineering, context engineering, retrieval-augmented generation, and AI agents, offering tutorials, papers, tools, and courses to optimize the use of large language models.
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