Dear learner,

Retrieval Augmented Generation (RAG) has emerged as a pivotal use case for large language models (LLMs), allowing these models to connect to an organization's proprietary data.

Our latest course, Building and Evaluating Advanced RAG Applications, will give you the tools to enhance retrieval techniques for obtaining coherent contexts (rather than getting random blocks of text) and employ evaluation metrics to iterate efficiently to a good system. This course was created in collaboration with TruEra and LlamaIndex and is instructed by their founders, Jerry Liu and Anupam Datta.

What you’ll learn:

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

Learn how to efficiently bring Retrieval Augmented Generation (RAG) into production by enhancing retrieval techniques and mastering evaluation metrics.

• 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.