Building LLMs for Production: Enhancing LLM Abilities and Reliability

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Building LLMs for Production: Enhancing LLM Abilities and Reliability构建面向生产的大模型：提高大模型能力和可靠性
（英文，可编辑的pdf电子文档）

Textbook
Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG
Author(s): Louis-François Bouchard; Louie Peters

Course description:
This course provides an end-to-end resource for students to enhance their skills or dive into the world of AI and develop their understanding of Generative AI and Large Language Models (LLMs). It explores various methods to adapt “foundational” LLMs to specific use cases with enhanced accuracy, reliability, and scalability. Written by over 10 people on our Team at Towards AI and curated by experts from Activeloop, LlamaIndex, Mila, and more, it is a roadmap to the tech stack of the future.
The course aims to guide developers through creating LLM products ready for production, leveraging the potential of AI across various industries. It is tailored for readers with an intermediate knowledge of Python.
Hands-on Guide on LLMs, Prompting, Retrieval Augmented Generation (RAG) & Fine-tuning
Roadmap for Building Production-Ready Applications using LLMs
Fundamentals of LLM Theory
Simple-to-Advanced LLM Techniques & Frameworks
Code Projects with Real-World Applications
Colab Notebooks that you can run right away
Community access and our own AI Tutor



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关键词：reliability production liability Enhancing Abilities

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