2025-09-24 –, Katherine Johnson @ TNW City
Retrieval-Augmented Generation (RAG) systems rely heavily on the quality of the retrieval process to generate accurate and contextually relevant outputs. In this 90-minute tutorial, we explore practical techniques to enhance retrieval across three key stages: pre-retrieval, mid-retrieval, and post-retrieval. Participants will learn how to optimize data preparation, query strategies, reranking, and evaluation to significantly improve the performance of RAG systems. A real-world case study will guide attendees through implementing these methods in a complete retrieval workflow.
The tutorial provides a structured approach to optimizing retrieval in RAG pipelines. We'll cover:
* Pre-retrieval optimization: how to preprocess and structure data, choose appropriate embeddings, and design efficient indexing and caching mechanisms.
* Mid-retrieval optimization: strategies for query expansion, context-aware reformulation, and reranking retrieved documents for relevance.
* Post-retrieval optimization: methods for refining and filtering results, incorporating user feedback, and continuously evaluating system performance.
Participants will explore practical techniques and open-source tools to operationalize enhanced retrieval at each of these stages. We’ll demonstrate the full workflow through a detailed case study, showing how to implement different techniques at each stage to build a more efficient and intelligent retrieval pipeline.
Outline:
- Introduction to Retrieval in RAG Systems
- Case Study Walkthrough: Applying All Stages
- Pre-Retrieval Optimization
- Data preprocessing
- Embedding techniques
- Indexing strategies
- Pre-filtering and caching
- Mid-Retrieval Optimization
- Query reformulation and expansion
- Contextual query reframing
- Recursive summarization frameworks
- Reranking techniques
- Distributed retrieval methods
- Post-Retrieval Optimization
- Result filtering and refinement
- Contextual relevance using chat history
- Feedback loops and active learning
- Evaluation metrics and continuous monitoring
- Q&A and Wrap-up
Background Knowledge Required:
* Familiarity with Python
* Basic understanding of LLMs
Target Audience: ML engineers, data scientists, developers working with LLMs in production, and anyone looking to learn how to build robust AI workflows using open source tools.
Aarti Jha is a Senior Data Scientist at Red Hat, where she develops AI-driven solutions to streamline internal processes and reduce operational costs. She brings over 6.5 years of experience in building and deploying data science and machine learning solutions across industry domains. She has been an active part of the PyData community and presented at PyData NYC 2024.
Mahima Arora is a Senior Data Scientist on the Data & AI team at Red Hat, specializing in Generative AI applications. She develops AI-powered solutions that enhance efficiency and effectiveness, leading initiatives to optimize AI systems for greater impact. An open-source enthusiast, Mahima continuously explores new tools and technologies to expand her expertise and stay at the forefront of innovation.