The reasoning engine, which was developed by a group of researchers at Salesforce over nearly a year, combines multiple large language models (LLMs), large action models (LAMs), the Atlas retrieval augmented generation (RAG) module, REST APIs, and different data connectors to datasets or knowledge repositories.
“In essence, for any given query that comes in, the Atlas system uses between eight and 12 different types of language models that are specialized for that particular subtest,” said Phil Mui, head of product and architecture at Salesforce AI, who also led the team developing Atlas.
These modules of the Atlas reasoning engine kick in once a user inputs a query and gets past the Einstein Trust Layer, which checks the query for abusive content, Mui explained, adding that the first step of the engine is to check and determine if the user input is valid or just chit-chat.
