High-quality data can lead to high-performing generative AI models. And while data audits, data integration and data preparation are typical aspects of the generative AI integration process, adding relevant context can further elevate data quality and result in more context-aware outputs.
One way to include context is to fine-tune a pretrained model on smaller datasets specific to your domain or real-world tasks and use cases. This helps save on the significant time, effort and cost associated with training models from scratch.
Meanwhile, both retrieval augmented generation (RAG) and Model Context Protocol (MCP) incorporate context in real time. A RAG system retrieves data from an external knowledge base, augments the prompt with enhanced context from the retrieved data and generates a response. MCP works similarly, but rather than adding context before generation as RAG does, MCP melds context during generation. It acts as a standardized layer for AI applications to connect to external data sources, services and tools, harnessing real-time data.