Six modules down. You now know what RAG is, how embeddings and vector stores work, how to choose a chunking strategy, how retrieval and reranking combine for better precision, how to evaluate a pipeline with RAGAS, and what changes when you deploy at scale. This capstone turns that knowledge into something real: three practical challenges that result in a working RAG design you can implement this week.
What You Covered
Module 1
What Is RAG
The three-component architecture: retriever, context assembler, generator. Why RAG beats static fine-tuning for dynamic knowledge, and the RAG vs fine-tuning decision matrix.
Module 2
Embeddings and Vector Stores
Dense vector representations, cosine similarity, and the vector store ecosystem: FAISS for local deployment, Pinecone and Weaviate for managed scale, pgvector for existing Postgres infrastructure.
Module 3
Chunking Strategies
Fixed-size, sentence-boundary, recursive paragraph, and document-aware chunking. Chunk size vs overlap tradeoffs and how they affect retrieval precision and recall.
Module 4
Retrieval and Reranking
Sparse BM25 vs dense DPR retrieval. Hybrid retrieval with Reciprocal Rank Fusion. Cross-encoder reranking as a two-stage pipeline that can lift precision from 62% to 91%.
Module 5
RAG Evaluation
The three evaluation axes: retrieval quality, generation faithfulness, answer relevance. RAGAS four-metric framework: context precision, context recall, faithfulness, answer relevance.
Module 6
Deploying RAG
Latency budgets across embedding, retrieval, and generation stages. Three caching layers, metadata filtering, observability logging, and advanced patterns: HyDE, FLARE, self-RAG.
Your Three Practical Challenges
Each challenge produces a real artifact you can build on or deploy. Do not skip to the certificate: the challenges are the course.
Three challenges to complete this week
1
Build a RAG pipeline on a real dataset. Choose a corpus you actually use: internal documentation, a set of research papers, product manuals, or support tickets. Chunk it with LangChain's RecursiveCharacterTextSplitter (chunk size 512, overlap 64), embed with Sentence-BERT (all-MiniLM-L6-v2), index in FAISS, and wire a simple query interface. Ask ten real questions a colleague might ask. Record which answers are correct, which are plausible but wrong, and which are clearly off. This baseline log is your evaluation starting point.
2
Design the chunking strategy for your domain. Review the ten failed answers from Challenge 1. For each failure, identify whether it was a chunking problem (the answer was split across chunk boundaries), a retrieval problem (the right chunk was not returned), or a generation problem (the chunk was retrieved but the answer was still wrong). Based on this audit, choose a revised chunking approach: sentence-boundary or document-aware chunking typically helps when answers span multiple sections. Re-chunk, re-index, and repeat the ten queries. Document whether precision improved.
3
Evaluate with RAGAS on 20 questions. Build a 20-question evaluation set: the question, the ground-truth answer, and (from your pipeline) the generated answer plus retrieved context chunks. Run the RAGAS evaluate() function with all four metrics: context_precision, context_recall, faithfulness, and answer_relevance. Identify which metric is lowest. If faithfulness is the problem, your chunking is likely including contradictory or irrelevant context. If answer_relevance is low, your prompt may be instructing the model to be too verbose. Write one sentence describing the change you would make next.
After completing the three challenges, you have a documented baseline, a revised chunking strategy, and a RAGAS score across four dimensions. That is a production-grade evaluation artifact, not just a notebook experiment.
Your Certificate
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Arjun Jaggi · Free AI University
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Retrieval-Augmented Generation
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