Multi-modal large language models (LLMs) can understand text, images, or videos and with their ever increasing context size, they open up interesting use cases for application developers. At the same time, LLMs often suffer from hallucinations (fake content), outdated information (not based on the latest data), reliance on public data only (no private data), and a lack of citations back to original sources. In this talk, we’ll first take a tour of Gemini, Google’s multi-modal LLM, show what’s possible, and how to integrate it with your applications. We’ll then explore various techniques to overcome common LLM pitfalls, including Retrieval-Augmented Generation (RAG) to enhance prompts with relevant data, ReACT prompting to guide LLMs in verbalizing their reasoning, Function Calling to grant LLMs access to external APIs, and Grounding to link LLM outputs to verifiable information sources, and more.