Explore techniques to overcome common Large Language Model (LLM) pitfalls in this 45-minute conference talk from Devoxx. Begin with a quick overview of the latest advancements in multi-modal LLMs, understanding their capabilities and limitations. Dive into various strategies to improve LLM outputs, including response schemas for better formatting, Retrieval-Augmented Generation (RAG) to enhance prompts with relevant data, and Function Calling to integrate external APIs. Learn about Grounding techniques to link LLM outputs to verifiable information sources, addressing issues such as hallucinations, outdated information, and lack of citations. Gain insights on how to create more reliable and practical LLM applications for real-world use cases.
Avoid Common LLM Pitfalls - Techniques for Enhancing LLM Outputs
Overview
Syllabus
Avoid common LLM pitfalls by Mete Atamel
Taught by
Devoxx
Related Courses
-
Techniques to Avoid Common LLM Pitfalls - Overcoming Hallucinations and Limitations
-
Finetuning Large Language Models
-
LangChain Chat with Your Data
-
Measuring Hallucinations in RAG - Retrieval Augmented Generation
-
LLM Projects - A Quick Tutorial on Multi-Agent Workflows with AutoGen
-
Retrieval Augmented Generation (RAG) for LLM-Powered Apps
