Designing Large Language Model Applications: A Holistic Approach to LLMs

A comprehensive overview of designing large language model applications, praised for its synthesis of advanced AI methods and practical applications. It explores fundamental concepts like prompting, fine-tuning, and reasoning, emphasizing how LLMs behave in practice through hands-on exercises and numerous research paper references. The text examines the ingredients of LLMs, including pre-training data, vocabulary, and tokenization, and details their architecture and learning objectives. It further investigates advanced techniques for fine-tuning, mitigating hallucinations, improving reasoning, and optimizing LLM inference, while also covering interfacing LLMs with external tools and the retrieval-augmented generation (RAG) pipeline. The book concludes by discussing LLM application paradigms, aiming to equip readers with the intuition and tools for building production-grade LLM solutions.

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