Avoid common LLM pitfalls

It’s easy to generate content with a Large Language Model (LLM), but the output is often badly formatted, suffers from hallucinations (fake content), outdated information (not based on the latest data), reliance on public data only (no private data), and a lack of citations back to original sources. Not ideal for real-world applications. In this talk, we’ll provide a quick overview of the latest advancements in multi-modal LLMs, highlighting their capabilities and limitations. We’ll then explore various techniques to overcome common LLM pitfalls, including response schemas to tame the LLM outputs, Retrieval-Augmented Generation (RAG) to enhance prompts with relevant data, Function Calling to enhance LLMs with external APIs, and Grounding to link LLM outputs to verifiable information sources, and more.