RAG and search
Connect a model to your own documents with embeddings, search and retrieval.
21tutors
5teachers
3free to start
$4 to $11per paid lesson
RAG and search tutors
21 tutors
Planning a RAG Pilot for Company Docs
Planning a RAG Pilot for Company Docs
Scope a small, measurable pilot for an assistant over your company documents before anyone builds too much.37 lessonsSampleFumiko Arai$4Parsing PDFs and Messy DocumentsParsing PDFs and Messy Documents
Get clean, well ordered text, tables and page numbers out of PDFs, scans, slides and office files.36 lessonsSampleFumiko Arai$5Reranking Retrieved ResultsReranking Retrieved Results
Add a reranking stage that puts the truly relevant passages first, within your latency and cost budget.35 lessonsSampleEmeka Nwosu$10Citations and Grounded AnswersCitations and Grounded Answers
Make RAG answers cite the right sources and check that every claim is actually supported by them.23 lessonsSampleFumiko Arai$7Questions over Tables and DatabasesQuestions over Tables and Databases
Answer questions over spreadsheets, document tables and databases correctly, with text to SQL done safely.Fumiko Arai$8Keyword Search and Inverted IndexesKeyword Search and Inverted Indexes
Understand how keyword search really works, from inverted indexes to BM25, and tune it for your content.Emeka Nwosu$6Keeping a RAG Index FreshKeeping a RAG Index Fresh
Keep retrieval in sync with changing documents: updates, deletions, versions, permissions and re-embedding.Fumiko Arai$10Hybrid Search: Keywords Plus VectorsHybrid Search: Keywords Plus Vectors
Combine keyword and vector search so you find both exact codes and loosely worded questions.Emeka Nwosu$8Long Context Versus RetrievalLong Context Versus Retrieval
Choose between sending whole documents to a long context model and retrieving passages, using evidence.Fumiko Arai$6Teachers who teach RAG and search
They wrote the lesson plans these tutors follow.
Neha Varadan
Fine tuning with judgment: when to do it, how to do it well, and how to know it workedfine tuning strategy, dataset preparation, LoRA and parameter efficient methods9 tutorsSampleNoor Siddiqui
Computer vision taught through real images, real failure cases and careful evaluationimage classification, object detection, segmentation9 tutorsSampleEmeka Nwosu
Search engineer teaching embeddings, chunking, vector and keyword search, and reranking from first principlesembeddings, chunking, vector databases9 tutorsSampleMateo Rojas
Generative models and representation learning, explained with intuition first and maths secondtransfer learning, autoencoders, GANs9 tutorsSampleFumiko Arai
Takes retrieval systems from demo to dependable: parsing, citations, freshness, retrieval evaluation and debuggingdocument parsing, citations and grounding, index freshness9 tutorsSampleMore in Building with AI
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