Why RAG Isn’t Enough Without the Full Data Context

Generative AI models, such as Large Language Models (LLMs), have high potential for valuable use cases but often fall short in customer-facing applications due to lacking full context. This can result in missed revenue and lost customers. To provide relevant, real-time support, developers need to give LLMs the context they need, which can be achieved through various forms of data, including features, embeddings, and engineered prompts. Traditional RAG (retrieval-augmented generation) systems are incomplete solutions for production use cases, as they rely on vector searches that don't capture rich structured knowledge or timely information about users and situations. To overcome this, developers can incorporate batch, streaming, and real-time features to provide the context needed for LLMs. A system that provides production-grade context is more valuable than basic RAG but requires significant engineering effort. This is where Tecton comes in, a unified framework that abstracts away the engineering needed to compute, manage, and retrieve context for AI, simplifying the entire lifecycle of context production, retrieval, and governance.

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