Are you ready to dive headfirst into the world of Large Language Models (LLMs) and make your company the talk of the town—for all the wrong reasons? Join me in this fun (and slightly terrifying) journey through the pitfalls of developing production-grade LLM apps. We’ll cover everything you need to know to become (in)famous:

• RAG and the Dangers of Retrieval-Augmented Generation: Because who doesn’t love when your model confidently spews out fake news?
• Guardrails: The Training Wheels You Shouldn’t Ditch: Unless, of course, you enjoy watching your LLMs careen off the ethical cliff taking your regulatory obligations with it.
• Content Safety: It’s like playing Russian roulette with your brand—one bad response, and boom!
• Red Teaming: Or how to let a bunch of hackers poke holes in your shiny new app before the real ones do.
• Templated Answers: These are your secret weapon for controlling RAG-based answers and reducing hallucinations—because sometimes, predictable is good.
• Continuous Evaluation: This is how you ensure the quality of responses doesn’t degrade as your app evolves—because there’s nothing worse than fixing one thing and breaking three others.
• Post-Production Evaluation: This is where you make sure your system works with real customer data as it evolves—because catching issues before they make the news is always a win.
• Observability: Or as I like to call it, "How to spot the disaster before your customers do." Because when things go sideways, you’ll want to explain how those responses were generated—and hopefully, learn from it.
• Prompt Injection: Because letting users hijack your LLM’s prompts is always a fun surprise.
• Cost of LLM Calls & Scalability: Want to really upset your accounting team? Just let your LLM run wild with no regard for cost or scalability. It’s all fun and games until someone sees the bill!

If you’re serious about making an impact (or just making headlines), this talk is for you. We’ll laugh, we’ll cry, and we’ll learn how to avoid turning your company into an (in)famous cautionary tale.