Building and Evaluating Advanced RAG Applications
Jerry Liu and Anupam Datta
Free for a limited time
- 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 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.
Who should join?
Anyone with basic Python knowledge interested in how to effectively employ the latest methods in Retrieval Augmented Generation (RAG).
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