RAG
"Getting the right data to ground the answers or scope the actions of an agent is critical; you want to give an LLM the least amount of the most relevant information..."
"Using actual user conversations to generate additional attributes and ontologies"
"We use our own custom LLM so that it doesn't hallucinate"
New approach combines VectorRAG and GraphRAG to improve local search performance and drive down global search costs.
Retailer joins the RAG trade with the release of a large language model (LLM) that generates responses for staff to read out to customers.
"You can create a consumer, a brand strategist, a brand marketer, client, encoded with actual ground truth data, then critique the content that's been generated by the system with agents playing off against each other."
"Here's where people end up in RAG hell, with a bunch of unfamiliar tools and in many cases immature tools...”