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Retrieval Augmented Generation Applications by DeepLearning AI, LlamaIndex & TruEra [Free; Python]: Join Now!

Apoorva

Dec 6, 2023
2 min read

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Online applications are invited for the Building and Evaluating Advanced Retrieval Augmented Generation Applications by DeepLearning AI, LlamaIndex & TruEra.

About DeepLearning AI

DeepLearning AI is an education technology company that is empowering the global workforce to build an AI-powered future through world-class education, hands-on training, and a collaborative community. DeepLearning AI has created high-quality AI programs on Coursera that have gained an extensive global following. By providing a platform for education and fostering a tight-knit community, DeepLearning AI has become the pathway for anyone looking to build an AI career.

Retrieval Augmented Generation Applications

About LlamaIndex

LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models.

About TruEra

TruEra’s AI Quality solutions explain, debug, and monitor machine learning models, leading to higher quality and trustworthiness, as well as faster deployment. Backed by years of pioneering research, TruEra works across the model lifecycle, is independent of model development platforms, and embeds easily into your existing AI stack.

Course Details

Learn methods like sentence-window retrieval and auto-merging retrieval, improving your RAG pipeline’s performance beyond the baseline.
Learn evaluation best practices to streamline your process, and iteratively build a robust system.
Dive into the RAG triad for evaluating the relevance and truthfulness of an LLM’s response: Context Relevance, Groundedness, and Answer Relevance.

What you’ll learn in this course?

Retrieval Augmented Generation (RAG) stands out as one of the most popular use cases of large language models (LLMs). This method facilitates the integration of an LLM with an organization’s proprietary data.

To successfully implement RAG, it is essential to enhance retrieval techniques for obtaining coherent contexts and employ effective evaluation metrics.

In this course, we’ll explore:

Two advanced retrieval methods: Sentence-window retrieval and auto-merging retrieval perform better compared to the baseline RAG pipeline.
Evaluation and experiment tracking: A way to evaluate and iteratively improve your RAG pipeline’s performance.
The RAG triad: Context Relevance, Groundedness, and Answer Relevance, are methods to evaluate the relevance and truthfulness of your LLM’s response.