The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

The Building Blocks of Agentic Systems with Harrison Chase - #698

Aug 19, 2024

Harrison Chase, co-founder and CEO of LangChain, shares insights from his extensive background in machine learning and MLOps. He discusses the evolution of agentic systems, emphasizing their real-world applications and communication needs. Harrison delves into Retrieval-Augmented Generation (RAG) and the importance of observability tools for enhancing agent development. He also highlights the challenges of transitioning prototypes to production and offers his hot takes on prompting and multi-modal models, providing a glimpse into the future of LLM applications.

59:17

Episode guests

Harrison Chase

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Episode notes

Podcast summary created with Snipd AI

Quick takeaways

Harrison Chase discusses the evolution of LangChain from an orchestration middleware framework to a comprehensive suite including LangSmith and LangGraph for enhanced agent-based applications.

The podcast highlights the significance of RAG techniques in enriching LLM responses by integrating external knowledge, especially beneficial in customer support scenarios.

Chase emphasizes the importance of observability and real-time performance tracking through LangSmith, which aids developers in continuously evolving their AI applications based on user feedback.

Deep dives

Background and Rise of Langchain

The co-founder and CEO of Langchain, Harrison Chase, shares his journey in the machine learning field, highlighting his experience in MLOps and machine learning teams in fintech. His exploration into the world of language models began around the time Stable Diffusion was released. Recognizing patterns in projects built on OpenAI APIs, Chase aimed to abstract these patterns into a Python package, leading to the creation and launch of Langchain shortly before ChatGPT gained popularity. The framework has since rapidly grown, featuring over 15 million monthly downloads, in addition to a thriving community contributing to a network of integrations with numerous LLM and vector store providers.

Agent Use Case: Gather Information in Workflow

01:31

Effective Communication is Key

01:46

Dynamic Few-Shot Prompting

00:58

Bridge the Gap from Prototype to Production

01:20

Intro

2min

The Birth and Growth of LangChain

3min

The Evolution of Agentic Systems

26min

Enhancing Agent Development Through Observability Tools

3min

Navigating AI Frameworks: Langraph and RAG Insights

10min

Evolving Evaluation for LLMs

7min

Building LLM Applications: User Experience and Innovation

8min

Today, we're joined by Harrison Chase, co-founder and CEO of LangChain to discuss LLM frameworks, agentic systems, RAG, evaluation, and more. We dig into the elements of a modern LLM framework, including the most productive developer experiences and appropriate levels of abstraction. We dive into agents and agentic systems as well, covering the “spectrum of agenticness,” cognitive architectures, and real-world applications. We explore key challenges in deploying agentic systems, and the importance of agentic architectures as a means of communication in system design and operation. Additionally, we review evolving use cases for RAG, and the role of observability, testing, and evaluation tools in moving LLM applications from prototype to production. Lastly, Harrison shares his hot takes on prompting, multi-modal models, and more!

The complete show notes for this episode can be found at https://twimlai.com/go/698.

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