Wave 1: The Co-Pilot Era (Present): AI supports human testers by automating repetitive tasks like log analysis and test case generation. Tools likeUIPathand RAG Models are central to this era. Wave 2: The Agentic Era (Near Future): AI evolves into autonomous agents capable of managin...
5 What is Retrieval Augmented Generation (RAG) Understand its Architecture 6 End to end flow in RAG Architecture and its key advantages Understand RAG (Retrieval Augmented Generation) – LLM Architecture with Usecase 7 Misconceptions – Why RAG LLM’s – cant we solve problem with traditional meth...
We introduce Plan*RAG, a novel framework that enables structured multi-hop reasoning in retrieval-augmented generation (RAG) through test-time reasoning plan generation. While existing approaches such as ReAct maintain reasoning chains within the language model’s context window, we observe that this ...
It uses the spec syntax for test case structure (describe,test,before,after). Reasons: No test-case name-clashes when using describe. Not forgetting to call super in setup/teardown methods. No nesting of describe blocks. IMO nesting of those blocks is an anti-pattern. ...
You will learn how Retrieval-Augmented Generation (RAG) works and how it enhances AI output by integrating external information into the language model’s prompts. This technique helps AI code assistants access a wider range of code elements and documentation, leading to more accurate and relevant ...
He noted that choosing the appropriate evaluation metrics—whether intrinsic or extrinsic—is crucial and should be tailored to the specific use case and application of the RAG system. Anand discusses about the Retrieval-Augmented Generation (RAG) framework, crucial for evaluating models that blend ...
You will be building a question-answering system with LLMs using theretrieval augmented generation(RAG) framework. RAG combines the power of LLMs with a retrieval mechanism that searches a database or knowledge source for relevant information, grounding its responses in specific, retrieved content ra...
We introduce Plan*RAG, a novel framework that enables structured multi-hop reasoning in retrieval-augmented generation (RAG) through test-time reasoning plan generation. While existing approaches such as ReAct maintain reasoning chains within the language model's context window, we observe that this ...
Generative AI integrates with orchestration code, routing logic, and an index for retrieval-augmented generation (RAG), which complicates evaluation. Although you should assess the models individually by using metrics, it's also important to evaluate other system components. ...
apis.tools import RAGToolRuntime, ToolGroups, ToolRuntime from llama_stack.apis.vector_dbs import VectorDBs from llama_stack.apis.vector_io import VectorIO from llama_stack.distribution.datatypes import StackRunConfig from llama_stack.distribution.datatypes import Provider, StackRunConfig from llama_...