Part 9/13:
A key breakthrough in making AI models more relevant and accurate is Retrieval-Augmented Generation (RAG). Instead of relying solely on pre-trained models, RAG dynamically queries enterprise data sources, feeding contextual, up-to-date information into AI outputs.
How RAG Works with Starburst
Retrieval: When a user poses a question, Starburst fetches the most relevant data—such as recent research papers, logs, or transaction records—via vector search or text matching.
Prompting: This data is then incorporated within prompts sent to large language models (LLMs) like OpenAI or on-premise models, enriching the context.
Generation: The AI responds with answers grounded in actual enterprise data, improving accuracy, relevance, and timeliness.