RAG: End to End

Build a retrieval-augmented generation system from chunking to evaluation.

6 lessons · about 49 minutes of reading

1
RAG Explained: What Retrieval-Augmented Generation Is and When to Use It
Learn how RAG lets an LLM answer from your own documents, the architecture behind it, and when to choose it.
8 min read
2
RAG Step 1: Loading and Chunking Documents
Extract text from files, split it into overlapping chunks, and attach metadata so retrieval returns useful passages.
8 min read
3
RAG Step 2: Embeddings and Vector Search
Turn chunks into embedding vectors and find the most relevant ones for a question using cosine similarity.
8 min read
4
RAG Step 3: Building a Reusable Retrieval Pipeline
Wrap embeddings and search into a small vector store class with metadata, score thresholds, and saving to disk.
8 min read
5
RAG Step 4: Generating Grounded Answers with Citations
Build the prompt from retrieved chunks, instruct the model to stay grounded, and return answers with source citations.
8 min read
6
RAG Step 5: Evaluation and Production Best Practices
Measure retrieval and answer quality, improve with hybrid search and reranking, and prepare a RAG system for production.
9 min read

RAG Explained: What Retrieval-Augmented Generation Is and When to Use It