The latest articles on DEV Community by Yam Marcovitz (@kichanyurd). https://dev.to/kichanyurd https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F2561621%2F74a8b7f7-9274-417c-9b83-be5ada9beedc.png https://dev.to/kichanyurd en Yam Marcovitz Thu, 12 Dec 2024 11:57:11 +0000 https://dev.to/kichanyurd/rethinking-how-we-train-customer-facing-ai-agents-3g8i https://dev.to/kichanyurd/rethinking-how-we-train-customer-facing-ai-agents-3g8i <p><strong>Current approaches to customer-facing AI agents all have serious limitations:</strong> Fine-tuning is expensive and inflexible, requiring complete retraining for simple changes while risking data leaks. RAG systems help with knowledge but can't guide behavior. Graph-based frameworks become overwhelmingly complex as agents grow more sophisticated.</p> <p><strong>The core challenge isn't just a technical ability to reach accurate results</strong>—it's about being able to shape and update agent behavior easily and dynamically, just like you'd guide human customer service representatives.</p> <p>See full blog post: <a href="https://www.parlant.io/blog/rethinking-how-we-build-customer-facing-ai-agents/" rel="noopener noreferrer">https://www.parlant.io/blog/rethinking-how-we-build-customer-facing-ai-agents/</a></p> langchain rag llm chatgpt