Introduction

Retrieval Augmented Generation (RAG) applications combine information retrieval and generative AI to provide accurate, context-aware answers by using large datasets and advanced search techniques.

Imagine you're a solution architect at a mid-sized enterprise tasked with building an internal knowledge assistant for employees to query company policies. The current system struggles with slow response times and often retrieves irrelevant or outdated information, frustrating users. Your team decides to implement a Retrieval Augmented Generation (RAG) application using Azure Database for PostgreSQL and Azure OpenAI. However, as the dataset grows to millions of rows, challenges like ensuring fast retrieval, maintaining accuracy, and avoiding AI-generated responses that might be incorrect. Additionally, complex queries involving multiple concepts, such as 'What are the vacation policies for HR in Europe?' require precise filtering and ranking. To address these issues, you need to optimize retrieval speed, improve accuracy through advanced indexing and chunking strategies, and explore innovative solutions like lightweight knowledge graphs. This module guides you through building and refining a scalable RAG pipeline tailored to such real world challenges.

After completing this module, you can:

• Explain the RAG pattern and its benefits for grounding large language models.
• Optimize similarity searches for large-scale datasets by implementing vector indexes like IVFFlat (Inverted File with Flat Compression), HNSW (Hierarchical Navigable Small World), and DiskANN (Disk Approximate Nearest Neighbor) in PostgreSQL.
• Build a complete RAG pipeline that retrieves relevant data chunks and generates context-grounded answers using Azure Database for PostgreSQL and Azure OpenAI.
• Apply advanced retrieval techniques, including hybrid search, semantic ranking, chunk optimization, and GraphRAG to improve precision and recall.