RAG in AI Tutorial

The Problem RAG Solves Large language models are trained once and answer from memory. They can hallucinate, go stale, and know nothing about your private documents. ...

Five Stages Every RAG system follows the same pipeline: ingest documents, split them into chunks, embed the chunks, store vectors with metadata, then retrieve and generate on each ...

Why Chunk Boundaries Matter Search works on chunks, not whole documents. A chunk that cuts a sentence in half is hard to retrieve and confusing to answer from; a chunk that mixes ...

The Embedding Layer Retrieval quality starts with the embedding model. A model that captures meaning well retrieves the right chunks; a weak model limits everything downstream no ...

Dense Retrieval Dense retrieval embeds the question and chunks, then finds the closest vectors with a vector database. It understands meaning, so synonyms and rephrased questions ...

What to Compare Choose a store by filters, ANN index, persistence, operational load and cost. Start simple; move to a heavier engine only when data or traffic demands it. ChromaDB ...

Write a Grounding Prompt The prompt tells the model where its answers may come from. Without it, the model drifts back to memory. Requiring Citations Ask the model to reference ...

Metadata Pre-Filtering Narrow the candidate set before ranking so selective filters do not waste distance computation. Store source, date, author and type on every chunk. MMR for ...

Routing Questions Not every question needs the vector store. Summarization tasks, math and small talk can skip retrieval entirely, saving latency and cost. Agentic Retrieval An ...

The Follow-Up Problem "What about pricing?" means little alone. The system must fold the conversation history into the retrieval query. Rewrite Before Retrieving A better ...

What to Measure Context relevance - Did retrieval find the right notes? Faithfulness - Does the answer stick to the context? Answer relevance - Does the answer address the ...

Multimodal Indexing Images, charts and slides follow the same pattern: a multimodal embedding model converts each one to a vector, and it becomes retrievable next to text chunks. ...

Three Ways to Add Knowledge RAG - Fetch relevant text at answer time. Fine-tuning - Train the model further on your data. Long context - Stuff the whole corpus into the prompt. ...

The Goal Drop a PDF, ask a question, get an answer grounded in the document. This is the classic RAG starter project and it forms the core of most support and knowledge tools. ...

Common Failures Stale vectors - Updated documents still return old chunks. Slow reranking - Reranking every query adds latency. Cost creep - Wide retrieval inflates prompt tokens. ...

Core Questions Q: What is RAG? A: Retrieval-Augmented Generation fetches relevant documents first, then asks the model to answer using them, grounding answers in real evidence. Q: ...