[Generative AI Chatbot] Decouple behavioral rules from prompt engineering

🔥 Customer-facing chat is the hardest technical problem in generative AI today. People who aren't in the trenches of this technology find this statement surprising. The assumption is that that's the one thing LLMs are actually good at... natural-language chats. But there's a growing understanding in the market right now that this isn't the case at all. For example, here's a new Harvard Business Review piece on why the most cutting-edge customer-facing AI agents still keep failing. (Credits: Andrew Shipilov, Nathan Furr, Sid Mohan, Jur Gaarlandt) Their observation: Customer interactions are unpredictable, complex, and high-risk -- all at the same time. This is what makes the challenge so great. The good news is that, in their experience, there is a solution to this problem: Treat the agent "like a toddler." Break up each task and feed it instructions one by one. That's when you can get the results you want. This is exactly the problem we've focused on for the last 2.5 years at Parlant. And one thing I'd add from experience: "one instruction at a time" is actually an engineering problem, not a prompting technique. Why? Because LLMs actually work well when handed a very small and focused task, without a lot of constraints or instructions that aren't strictly relevant to the active completion request. But when you expect them to figure out which 3 of the 50 behavioral rules currently matter, and follow them consistently, that's where they break. The lesson we've learned, though, is that if you can take those 50 rules and feed them into a system that seamlessly filters out all of the immediately irrelevant ones for the next current turn of the interaction -- the problem is solved. This approach is called Context Engineering, and it's what made Claude Code so much more powerful than previous coding agents - which mainly wrapped the LLM while providing it with too much context. What Claude Code does for AI coding, is exactly what Parlant (open-source) does for customer interactions. Highly recommend reading the full piece - it captures the problem really well. https://lnkd.in/dsVNiRZe