A CPO's perspective - real success with generative AI requires real-time data
LLMs are bringing AI to the mainstream, but how do companies bring contextualized and current data to their LLMs? Shaun Clowes of Confluent explores how data streaming platforms can help.
What is the role of real-time data in generative AI?
Real-time data is crucial for generative AI applications because it ensures that the AI has access to the most current and relevant information. Without real-time, contextualized data, generative AI may not provide meaningful insights or responses, leading to less effective outcomes. This is especially important in dynamic fields like customer service, where timely information can significantly enhance user experiences.
How can companies integrate domain-specific knowledge into LLMs?
Companies can integrate domain-specific knowledge into LLMs through two primary methods: prompt engineering and fine-tuning. Prompt engineering involves optimizing the input text to better communicate with the LLM, while fine-tuning requires retraining pre-trained models on specific datasets to adapt to particular business contexts. Both approaches aim to enhance the accuracy and relevance of the AI's outputs.
What benefits do data streaming platforms offer for generative AI?
Data streaming platforms enhance generative AI applications by providing real-time access to diverse data sources, breaking down data silos, and ensuring that the right data is available at the right time. This capability allows businesses to create more responsive and sophisticated AI-driven experiences, ultimately improving operational efficiency and customer satisfaction.
A CPO's perspective - real success with generative AI requires real-time data
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