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

About Brian Sletten

Brian Sletten is a liberal arts-educated software engineer with a focus on forward-leaning technologies. His experience has spanned many industries including retail, banking, online games, defense, finance, hospitality and health care. He has a B.S. in Computer Science from the College of William and Mary and lives in Auburn, CA. He focuses on web architecture, resource-oriented computing, social networking, the Semantic Web, AI/ML, data science, 3D graphics, visualization, scalable systems, security consulting and other technologies of the late 20th and early 21st Centuries. He is also a rabid reader, devoted foodie and has excellent taste in music. If pressed, he might tell you about his International Pop Recording career.

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