RAG Systems : Using LLMs with Guardrails
Large Language Models (LLMs) such as ChatGPT and Llama have impressed us with what they can do. They have also horrified us with what they actually do when they are employed with no protection: hallucinations, stale knowledge bases, no conceptual basis for reasoning, and a capacity for toxic and inappropriate content generation. Rather than avoid them altogether or risk legal liability or brand damage, we can put some guardrails around them to benefit from their best traits without fearing their worst.
Retrieval Augmented Generation (RAG) systems augment the process to make it behave more to our liking. Come hear what you can do to benefit from AI systems without fearing them.
We will cover examples using LangChain and LlamaIndex, two open source frameworks for working with LLMs and creating RAG infrastructure.
We will cover:
Introduction to LLMs
Risks and Limitations
Basic RAG Systems
Embeddings
Vector Databases
Prompt Engineering
Testing and Validating LLMs and RAG Systems
Advanced Techniques
AI as Judge
Zero fluff.