Avoid common LLM pitfalls
15:20 - 15:50, 27th of May (Tuesday) 2025 / DEV AI & DATA STAGE
It’s easy to generate content with a Large Language Model (LLM), but the output often suffers from hallucinations (fake content), outdated information (not based on the latest data), and reliance on public data only (no private data). Additionally, the output format can be chaotic, often littered with harmful or personally identifiable information (PII), and using a large context window can become expensive—making LLMs less than ideal for real-world applications.
In this talk, we’ll begin with a quick overview of the latest advancements in LLMs. We’ll then explore various techniques to overcome common LLM challenges: grounding and Retrieval-Augmented Generation (RAG) to enhance prompts with relevant data; function calling to provide LLMs with more recent information; batching and context caching to control costs; frameworks for evaluating and security testing your LLMs and more!
By the end of this session, you’ll have a solid understanding of how LLMs can fail and what you can do to address these issues.
