Rags and Tools and Bellyaches (federicopereiro.com)

Recent discussions in the AI/ML community highlight the emergence of advanced techniques like Retrieval-Augmented Generation (RAG) and Model Configuration Protocol (MCP), which enhance the utility of Large Language Models (LLMs). RAG focuses on improving the context provided to LLMs rather than altering their inherent weights, allowing models to pull relevant information from external databases or sources to enrich their responses. This reliance on more contextual data is crucial for delivering accurate and relevant answers, especially when users pose ambiguous queries. By utilizing embeddings, RAG can link related concepts without burdening the LLM with excessive data, enabling more intuitive search and response capabilities. Additionally, the introduction of tools expands the functionality of LLMs beyond simple text responses, empowering them to execute tasks like database queries and system operations. This is facilitated by the MCP, a protocol aimed at standardizing how these tools interact with LLMs, thus streamlining the process of retrieving and processing information. Importantly, fine-tuning remains a viable option for modifying a closed-source model’s behavior by providing targeted training data, while preserving the underlying weights. These advancements mark a significant step forward in developing more adaptable and context-aware AI systems, crucial for both developers and end-users alike.