Your First RAG Pipeline, End to End
Build a small retrieval augmented chatbot over your own documents, step by step, and see where it fails.
A taste of a lesson
My RAG bot gave a wrong answer about our holiday policy. Should I fix the prompt?
Not yet. First print the chunks that were retrieved for that question. If none of them contains the holiday policy rule, the prompt was never the problem: retrieval missed it, perhaps because the chunk is too large, the wording differs from the question, or k is too small. If the right chunk was retrieved and the answer is still wrong, then look at the prompt. This one check saves hours. Which chunks came back for that question?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Build a complete small RAG pipeline from documents to cited answers
- Chunk and embed documents and run similarity search
- Write a prompt that answers from sources and admits missing answers
- Test with realistic questions, including unanswerable ones
- Tell retrieval failures from generation failures when debugging
Lesson plan
- 1 What RAG does Explain retrieval augmented generation and plan a small project. Start
- 2 Preparing and chunking documents Turn documents into clean chunks with their source information. Start
- 3 Embedding and searching Embed chunks and find the most similar ones for a question. Start
- 4 The answering prompt Write a prompt that uses only retrieved passages and cites them. Start
- 5 Testing and debugging Evaluate answers and locate the cause of each failure. Start
- 6 Showing sources and next steps Present answers with sources and plan improvements. Start
Try asking
About this tutor
For beginners who can call a model API and want it to answer questions from their own documents. You build the whole pipeline at small scale: load and clean a handful of documents, split them into chunks, create embeddings, store them in a simple in memory index, retrieve the best chunks for a question, and write a prompt that answers only from those sources, cites them and admits when the answer is not there. Then you test it with a few real questions and learn to tell retrieval failures from generation failures, which is the most useful debugging skill in RAG.
Reviews
4.7
3 ratingsSample
- Sakura T.Sample
Free and well paced. I would have liked an example with PDFs, but the steps were very clear.
- Esther K.Sample
Built a working bot over our staff handbook in a weekend. The retrieval versus generation check is now the first thing I do when something goes wrong.
- Mohammed A.Sample
No framework, just arrays and a prompt, so I actually understand what is happening. Including unanswerable questions in testing was a great idea.
About the teacher
Search engineer teaching embeddings, chunking, vector and keyword search, and reranking from first principles
9 tutors 374 lessons taught Sample
I come from search: indexes, ranking and the long tail of queries that make a search box look foolish. When retrieval augmented generation arrived, most of what mattered turned out to be old search problems in new clothes, so that is how I teach it. We start with how text becomes something you can compare, then how documents are split,...
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