http://localhost:1313/chapter1/s6/index.html Work in progress This section is under construction. This information hasn’t been reviewed or edited yet! Introduction Now that we’ve explored the fundamentals of LLMs, key players in the market, deployment considerations, technical foundations, and the art of prompt engineering, it’s time to dive into how these models actually operate in real-world applications. This section will examine the technical aspects of inference—the process where LLMs generate responses to our inputs—focusing on API integration patterns, response handling strategies, knowledge integration techniques, and optimization methods. Understanding inference techniques is crucial for implementing LLMs effectively, whether you’re building a simple chatbot or a sophisticated enterprise application with access to proprietary knowledge. Hugo en http://localhost:1313/chapter1/s6/s6_activity/index.html Mon, 01 Jan 0001 00:00:00 +0000 http://localhost:1313/chapter1/s6/s6_activity/index.html Work in progress This section is under construction. This information hasn’t been reviewed or edited yet! Practical Activity Overview In this activity, you’ll build a Retrieval Augmented Generation (RAG) system that can answer questions based on your own documents. RAG combines the power of retrieval systems with language models to generate accurate, context-rich responses by: Retrieving relevant information from your documents Using that information to augment the language model’s knowledge Generating responses grounded in your specific data This implementation will follow a simple but effective architecture with two main components: