Augmented Language Models (LLM Bootcamp)

[@ayanghosh8226]

Love the logical sequence of presenting the complications in advanced LLM applications. One of the best resources on the web, if one wants a solid mental map of how and when to augment LLMs.

[@HarendraSingh-xw6hv]

💯

[@loic7572]

This is the best bootcamp I've ever watched. I only wish I had known about the KZbin channel before.

[@l501l501l]

Second that!

[@jeromeeusebius]

Great resource for understanding RAG and the various ways to improve reliability and accuracy of LLM's. Thanks for sharing.

[@RohanKumar-vx5sb]

u’re the best. this has been the singular most useful and up to date analysis of LLM advancements

[@lukeliem9216]

This talk is very informative about building LLM-based apps with proprietary datasets.

[@robertcormia7970]

Another fantastic video (webinar) helping to build on foundational knowledge of LLMs. Clear explainations of chains, tools, APIs, and "process". Can't wait to watch the next one (LLMOPs)

[@za_daleko]

Thanx for this knowledge. Greetings from Poland.

[@fudanjx]

Quick Summary: Introduction: Language models are powerful but lack knowledge of the world. We can augment them by providing relevant context and data. Witnesses: - Retrieval: Searching a corpus and providing relevant documents as context. - Chains: Using one language model to develop context for another. - Tools: Giving models access to APIs and external data. Testimonies: Retrieval: - Simplest way is adding relevant facts to context window. - As corpus scales, treat it as an information retrieval problem. - Embeddings and vector databases can improve retrieval. Chains: - Use one language model to develop context for another. - Can help encode complex reasoning and get around token limits. - Tools like Langchain provide examples of chain patterns. Tools: - Give models access to APIs and external data. - Chains involve manually designing tool use. - Plugins let models decide when to use tools. Key Takeaways: - Start with rules and heuristics to provide context. - As knowledge base scales, think about information retrieval. - Chains can help with complex reasoning and token limits. - Tools give models access to external knowledge. Conclusion: Augmenting language models with relevant context and data can significantly improve their capabilities. There are a variety of techniques to provide that augmentation, each with trade-offs around flexibility, reliability, and complexity.

Awesome! Thanks Josh for the presentation!

[@saratbhargavachinni5544]

Great talk! Thanks for sharing

[@צחייעקובוביץ]

great. I had to listen at a 0,75 speed, not to miss anything.

[@deeplearningpartnership]

Cool

[@domlahaix]

Crocodile, Ball.... unless you're working for Lacoste 😀

[@SavanVyas91]

What’s his name where can I find him?

[@kennethcarvalho3684]

isnt this a search like google