Get hands-on using LangChain to load documents and apply text splitting techniques with RAG and LangChain to enhance model responsiveness. Create and configure a vector database to …

This Fundamentals of Building AI Agents using RAG and LangChain course builds job-ready skills that will fuel your AI career. During this course, you’ll explore retrieval-augmented generation …

This allows you to test your prompt / model configuration over a series of inputs to see how well it generalizes across different contexts or scenarios, without having to write any code. Navigate …

Unlock the potential of LangChain and Large Language Models (LLMs) with this comprehensive course designed for AI enthusiasts and developers. From foundational concepts to building …

Retrieval Augmented Generation (RAG) is a technique that enhances Large Language Models (LLMs) by providing them with relevant external knowledge. It has become one of the most …

2024年2月13日 · From your code snippet, it seems like you're trying to use the QAEvalChain class to evaluate a student's answer against a given answer key using a custom rubric. The …

2024年12月2日 · To grade this student answer, I need to follow the given criteria. Step 1: The first criterion is to grade the student answers based ONLY on their factual accuracy relative to the …

Evaluation is the process of assessing the performance and effectiveness of your LLM-powered applications. It involves testing the model's responses against a set of predefined criteria or …

Here is the grade criteria to follow: (1) Grade the student answers based ONLY on their factual accuracy relative to the ground truth answer. (2) Ensure that the student answer does not …

Here is an example prompting it using a score from 0 to 10. The custom prompt requires 3 input variables: “query”, “answer” and “result”. Where “query” is the question, “answer” is the ground …