Session brief
Building Context-Aware Reasoning Applications with LangChain and LangSmith: Harrison Chase
Overview
This talk explores building context-aware reasoning applications by integrating language models into larger systems. It emphasizes that while language models are powerful, they require external context and capabilities to perform complex tasks. The presentation outlines various approaches for providing context and enabling reasoning, highlighting the engineering challenges and tooling needed to develop these sophisticated AI applications.
Who should watch
- AI Engineers
- Product Managers
- Builders of AI applications
- Those interested in advanced LLM system design
- Developers looking to integrate context and reasoning into their AI projects
Key takeaways
- Context can be provided to language models through instruction prompting, few-shot examples, retrieval augmented generation (RAG), and fine-tuning.
- Reasoning capabilities can be built using chains of language model calls, routers that direct logic, and agents that operate in loops with dynamic step selection.
- Autonomous agents represent a more advanced form of reasoning where the LLM implicitly handles planning, execution, and validation without predefined sequences.
- Key challenges in building these applications include orchestration (choosing the right cognitive architecture), data engineering (providing relevant context), and prompt engineering (crafting effective prompts).
- Evaluation of LLM applications is difficult due to a lack of data and suitable metrics, often relying on manual review, LLM-assisted evaluation, and user feedback.
- Collaboration between different roles like AI engineers and product managers is crucial, with evolving skill sets needed for this new domain.
- The field is still in its early stages, with rapid advancements expected in tooling and capabilities.
Notable quotes
*By themselves they don't know about current events and they cannot run the code that you write.*
*Behind all of these seemingly magical product moments there is an extraordinary feat of engineering.*
*The main thing that we think is it's still just extremely early on in the space.*
Unofficial community note. Prefer the recording for nuance.