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
Evaluating Retrieval Quality
Evaluating Retrieval Quality
Measure whether your retrieval finds the right passages with real queries, relevance labels and the right metrics.75 lessonsSampleFumiko Arai$11Image embeddings and visual searchImage embeddings and visual search
Build image similarity and text to image search, and measure whether results are actually relevant71 lessonsSampleNoor Siddiqui$7RAG Explained for Non EngineersRAG Explained for Non Engineers
Understand how AI assistants answer from company documents, what can go wrong and what to ask your team.66 lessonsSampleEmeka NwosuFreeVector Databases for BeginnersVector Databases for Beginners
Learn what a vector database does, when you need one and how to choose without falling for marketing.63 lessonsSampleEmeka Nwosu$4Fine tune, prompt or retrieve?Fine tune, prompt or retrieve?
Choose between prompting, retrieval and fine tuning for your problem, and know why59 lessonsSampleNeha VaradanFreeYour First RAG Pipeline, End to EndYour First RAG Pipeline, End to End
Build a small retrieval augmented chatbot over your own documents, step by step, and see where it fails.59 lessonsSampleEmeka NwosuFreeMetadata Filters and Permission Aware RetrievalMetadata Filters and Permission Aware Retrieval
Filter retrieval by date, product or tenant and make sure users only ever retrieve what they may see.57 lessonsSampleEmeka Nwosu$11Question answering, from extractive to generativeQuestion answering, from extractive to generative
Understand how QA systems find, read and generate answers, and how to tell when they should abstain56 lessonsSampleMateo Rojas$7Debugging a RAG App That Answers BadlyDebugging a RAG App That Answers Badly
Trace each bad RAG answer to its real cause, from missing documents to ignored passages, and fix the most common.56 lessonsSampleFumiko Arai$8Embeddings Explained for BuildersEmbeddings Explained for Builders
Understand what embeddings capture, what they miss and how to use them for search, grouping and matching.53 lessonsSampleEmeka Nwosu$4Chunking Documents for RetrievalChunking Documents for Retrieval
Split documents into retrievable chunks that keep their meaning, then tune chunk size with real measurements.41 lessonsSampleEmeka Nwosu$6When Retrieval Is the Wrong ToolWhen Retrieval Is the Wrong Tool
Decide when RAG fits a problem and when a database query, tool call, longer prompt or plain search fits better.40 lessonsSampleFumiko Arai$5Teachers 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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