Large language models (LLMs) rely on accurate context to provide useful output
Companies increasingly use LLMs for customer communication, decision-making, and automation. While LLMs are trained on extensive offline data, they also need relevant context for accurate inferences. This context comes either from the prompt or from external data sources.
Teams have connected LLMs to static or slow-changing knowledge bases, but it is significantly harder to provide the LLM access to a fast-changing knowledge base (e.g., when streaming or real-time data is involved). Real-time context is critical for time-sensitive tasks like:
- Ecommerce recommendations
- Media content recommendations
- Contextual customer support
- Supply chain and logistics optimization
- Real-time marketing strategy recommendations
Generating point-in-time accurate prompts from historical data for prompt tuning is difficult
Because LLM responses are dependent on the quality of the prompt as well as the context available at request time, it can be challenging to develop, evaluate, and select the best prompts in a scalable and scientific way. With a feature platform like Tecton, you can create prompts with point-in-time accurate context to identify the best-performing prompts for historic events.
Tecton helps you turbocharge your Generative AI Initiatives with real-time context
- Easily leverage streaming data to enable real-time context for generative AI
- Use powerful out-of-the-box or custom data aggregations to create, test, and deploy ML features to provide your LLMs with tailored context
- Address the demands of your Generative AI workflow and minimize costs with a robust, scalable, and cost-effective platform that seamlessly integrates with existing workflows and systems
- Eliminate offline/online skew to train your Generative AI models with point-in-time correct offline training data and use those same feature definitions for online inference
The enterprise-grade context delivery for Generative AI
With Tecton you can easily integrate LLMs into streaming feature pipelines to support real-time feature pipelines.