This course explores the development of Retrieval-Augmented Generation (RAG) applications using vector databases to retrieve relevant information and generate grounded responses. It focuses on the progression from building knowledge-base retrieval pipelines and integrating generative models to evaluating retrieval quality, optimising application performance, securing database access, and exposing complete RAG workflows through FastAPI.

Through structured lessons and practical demonstrations, you will learn how RAG applications combine vector-based retrieval with generative models to produce context-aware answers. You will build a local extractive knowledge-base answering pipeline, integrate a generative model to create grounded responses, add source references, and handle queries with insufficient retrieved context. You will also evaluate retrieval performance using Precision@k, Recall@k, and Mean Reciprocal Rank, assess context relevance and answer grounding, and optimise retrieval settings such as top-k, chunk size, and similarity thresholds. The course progresses from building database-backed RAG pipelines to evaluating retrieval and answer quality, testing database operations, and implementing secure API-based applications. It emphasizes structured logging, graceful error handling, credential management, and end-to-end application architecture. Rather than treating RAG as a simple combination of retrieval and text generation, it focuses on developing reliable, measurable, secure, and maintainable applications that connect embedding models, vector databases, retrievers, and generative models. By the end of this course, you will be able to: - Build a knowledge-base retrieval pipeline for extracting relevant information - Integrate generative models to produce grounded answers with source references - Handle unsupported queries using safe fallback mechanisms - Evaluate retrieval quality using Precision@k, Recall@k, and Mean Reciprocal Rank - Assess retrieved context relevance, answer grounding, and response quality - Test vector database CRUD operations, metadata filters, persistence, and retrieval - Optimise retrieval performance using top-k, chunk size, and similarity thresholds - Implement structured logging, graceful error handling, and secure credential management - Build, execute, and validate a FastAPI-based RAG application using Pinecone This course is ideal for aspiring AI engineers, machine learning practitioners, Python developers, data professionals, and learners interested in building reliable RAG applications with vector databases. A foundational understanding of Python, embeddings, vector similarity, and vector database operations is recommended. Prior experience with FastAPI or production-level RAG applications is not required. Join us to learn how to transform vector-based retrieval pipelines into complete RAG applications, evaluate and optimise their performance, secure database interactions, and build API-driven knowledge-base answering systems.