RAG 2.0 is an end-to-end search system divided into these stages: information extraction, document preprocessing, indexing, and retrieval. It cannot be orchestrated by reusing LLMOps tools designed for RAG 1.0 because these stages are coupled, lack unified APIs and data formats, and have circular...
Spring Festival evening party last night, I go to the village school volunteer, start time is 5:00, end is at 7:30. In the process, I first task was to check standing in the first stage, help the teacher is writing on the card 1, 2, 3, 4... Such as Numbers, to the guests ...
The significance of RAG in NLP cannot be overstated. Traditional language models, especially early ones, could generate text based on the data they were trained on but could not often source additional, specific information during the generation process. RAG fills this gap effectively, creating a b...
The new AI Model Onboarding Risk Identification questionnaire template is used during the model onboarding process to help identify the risks associated with a model. This questionnaire template is used in the Foundation Model Onboarding workflow. The new AI Use Case Risk Identification questionnaire ...
This is where Retrieval-Augmented Generation (RAG) steps in. RAG bridges the gap by allowing LLMs to access and process information from external sources, leading to more grounded and informative answers. While standard RAG excels at simple queries across a few documents, agentic RAG takes it ...
Agentic RAG is comparatively more sophisticated and dynamic. It can come up with questions of its own, create context from its memory, and carry out additional tasks without being explicitly asked to do so. This step beyond traditional RAG grants agentic RAG the ability to make more informed de...
July 2023 Step-by-Step Tutorial: Building ETLs with Microsoft Fabric In this comprehensive guide, we walk you through the process of creating Extract, Transform, Load (ETL) pipelines using Microsoft Fabric. June 2023 Get skilled on Microsoft Fabric - the AI-powered analytics platform Who is Fab...
RAG vs. fine-tuning Edge solutions for real-time decision making What is retrieval-augmented generation? What is an Ansible Role—and how is it used? AI infrastructure explained Understanding AI/ML use cases What is MLOps? What are large language models?
Documentation. Documentation is essential for understanding, maintaining, and improving AI agents. There are at least two main types of documentation to consider: Technical documentsmight include diagrams of the AI agent’s components, data flow, and decision-making processes along with records of any...
We're updating the administrator consent flow for the Webex Video Integration with Microsoft Teams setup process. There is no change for customers who have already configured this integration. The setup now has an additional authorization step, so you may be required to sign in twice with your...