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Learn to overcome critical challenges when implementing Large Language Models in production environments through this comprehensive conference talk. Discover how to address common LLM pitfalls including hallucinations, outdated information, reliance on public data only, chaotic output formats, harmful content generation, and high costs associated with large context windows. Explore the latest advancements in LLM technology before diving into practical solutions such as grounding techniques and Retrieval-Augmented Generation (RAG) for enhancing prompts with relevant data, function calling methods to provide LLMs with current information, and batching and context caching strategies to manage expenses effectively. Gain insights into frameworks for evaluating LLM performance and conducting security testing to ensure safe deployment. Master techniques to make LLMs more reliable and suitable for real-world applications while understanding both the failure modes and remediation strategies essential for successful LLM implementation.
