# LLM Bootcamp Source: https://fullstackdeeplearning.com/llm-bootcamp/spring-2023?s=08 ## Summary The LLM Bootcamp aims to bring learners fully up to date with state-of-the-art large language models and ready to build and deploy LLM apps, regardless of their prior machine learning experience. Its lectures cover prompt engineering, LLMOps, UX for language user interfaces, augmented language models, and a one-hour guide to launching an LLM app, along with ML foundations and open questions such as AGI safety. Invited talks include Reza Shabani on training your own LLM, drawing on his work on Replit's Ghostwriter, and Harrison Chase, co-creator of LangChain, on agents. The course is offered free of charge. ## Article What are the pre-requisites for this bootcamp? Our goal is to get you 100% caught up to state-of-the-art and ready to build and deploy LLM apps, no matter what your level of experience with machine learning is. Please enjoy, and email us, tweet us, or post in our Discord if you have any questions or feedback! Lectures Learn to Spell: Prompt Engineering High-level intuitions for prompting Tips and tricks for effective prompting: decomposition/chain-of-thought, self-criticism, ensembling Gotchas: "few-shot learning" and tokenization LLMOps Comparing and evaluating open source and proprietary models Iteration and prompt management Applying test-driven-development and continuous integration to LLMs UX for Language User Interfaces General principles for user-centered design Emerging patterns in UX design for LUIs UX case studies: GitHub Copilot and Bing Chat Augmented Language Models Augmenting language model inputs with external knowledge Vector indices and embedding management systems Augmenting language model outputs with external tools Launch an LLM App in One Hour Why is now the right time to build? Techniques and tools for the tinkering and discovery phase: ChatGPT, LangChain, Colab A simple stack for quickly launching augmented LLM applications LLM Foundations Speed-run of ML fundamentals The Transformer architecture Notable LLMs and their datasets What's Next? Can we build general purpose robots using multimodal models? Will models get bigger or smaller? Are we running out of data? How close are we to AGI? Can we make it safe? Invited Talks Reza Shabani: How To Train Your Own LLM The "Modern LLM Stack": Databricks, Hugging Face, MosaicML, and more The importance of knowing your data and designing preprocessing carefully The features of a good LLM engineer By Reza Shabani, who trained Replit's code completion model, Ghostwriter. Harrison Chase: Agents The "agent" design pattern: tool use, memory, reflection, and goals Challenges facing agents in production: controlling tool use, parsing outputs, handling large contexts, and more Exciting research projects with agents: AutoGPT, BabyAGI, CAMEL, and Generative Agents By Harrison Chase, co-creator of LangChain We are deeply grateful to all of the sponsors who helped make this event happen. We are excited to share this course with you for free. We have more upcoming great content. Subscribe to stay up to date as we release it. We take your privacy and attention very seriously and will never spam you. I am already a subscriber