Pluralsight – Expansion Strategies for RAGs

Unlock advanced query techniques! Learn to improve retrieval performance in RAG systems using NLP tools and Python code demonstrations, all designed for experienced developers looking to optimize AI-driven retrieval systems.
This practical course is designed for experienced software developers who want to enhance the performance of their Generative AI Retrieval-Augmented Generation (RAG) systems. In this course, Expansion Strategies for RAGs. you'll unlock advanced query understanding techniques. First, you'll begin with an in-depth exploration of query parsing and entity recognition using popular NLP libraries like SpaCy and Hugging Face. Then, you'll learn how to implement parsing techniques that extract essential information from queries, setting the foundation for optimized retrieval. Next, you'll dive into synonym and semantic expansion, where you'll explore how to expand queries using techniques like Word2Vec and pre-trained BERT models. These strategies will help you capture semantically related terms and improve the relevance of your retrieval results. Finally, you'll learn pseudo-relevance feedback, a powerful technique that uses initial retrieval results to refine and improve subsequent queries. By the end of the course, you'll have hands-on experience implementing and evaluating various query understanding and expansion techniques, using Python to boost the effectiveness of RAG systems.
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