September 2024 Summaries

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.

Tecton's expanded platform now supports generative AI, enabling organizations to build rich, context-aware AI applications that leverage diverse data types. The AI landscape is shifting towards generative AI, which lacks a true understanding of specific situations and individuals, requiring context to deliver real business value. Tecton's platform expansion addresses the challenges of providing context for generative AI models, including assembling knowledge, managing knowledge bases, retrieving relevant knowledge as context for inference, and optimizing this trade-off without breaking the bank. The new platform features managed embeddings, real-time data integration for LLMs, dynamic prompt management, and LLM-powered feature generation, unified context for any AI model, and provides a modular toolset to take AI applications to the next level with less effort and fewer resources.

Tecton is a platform that enables production-grade data pipelines through a declarative framework, making it easy to define ongoing streaming, batch, and real-time transformation of raw data into rich features that describe the current state of any business entity. Tecton's agent service provides up-to-date context for generative AI applications by building on-demand prompts enriched with feature data and delivering access to features as LLM tools. The agent service allows users to inject data in real-time into their LLM's system prompts, enabling personalized and timely responses. This is achieved through the use of enriched prompts, which allow users to include up-to-date information related to the session context, and features as tools, which provide additional data retrieval capabilities to the LLM so that it can answer specific questions related to the session context. Tecton integrates seamlessly with LangChain and LlamaIndex frameworks, enabling users to create more personalized, relevant, and up-to-date AI interactions. By leveraging Tecton's feature pipeline integration with LLMs, developers can significantly enhance their LLM applications, such as recommendation systems and customer support chatbots, by providing real-time context enrichment and data retrieval capabilities.