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Foundations 89

All Foundations89 AI basics25 How language models work23 AI safety and ethics49

Everyday AI 91

All Everyday AI91 Prompting20 ChatGPT and Claude at work25 AI for writing29 AI for research and study33

Building with AI 162

All Building with AI162 Python for AI21 Building with LLM APIs30 RAG and search21 Evaluation and testing21 AI agents31 AI automation25 AI coding assistants30

Machine learning 149

All Machine learning149 Machine learning35 Data science and statistics30 Math for AI23 Deep learning28 NLP14 Computer vision14 Fine tuning and training12 MLOps and deployment18

Creative AI 77

All Creative AI77 Image and video AI58 Audio and voice AI12 AI for design33

AI at work 145

All AI at work145 AI for business34 AI for marketing and sales23 AI for product and operations28 AI for education28 AI for professionals29 AI careers18

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RAG and search Intermediate Clear all

Image embeddings and visual search

Image embeddings and visual search

Build image similarity and text to image search, and measure whether results are actually relevantIntermediateComputer vision4.7(3)71 lessonsSampleNoor Siddiqui$7 RAG Explained for Non Engineers

RAG Explained for Non Engineers

Understand how AI assistants answer from company documents, what can go wrong and what to ask your team.All levelsRAG and search4.7(3)66 lessonsSampleEmeka NwosuFree Fine tune, prompt or retrieve?

Fine tune, prompt or retrieve?

Choose between prompting, retrieval and fine tuning for your problem, and know whyAll levelsFine tuning and training4.7(3)59 lessonsSampleNeha VaradanFree Question answering, from extractive to generative

Question answering, from extractive to generative

Understand how QA systems find, read and generate answers, and how to tell when they should abstainIntermediateNLP4.7(3)56 lessonsSampleMateo Rojas$7 Debugging a RAG App That Answers Badly

Debugging 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.IntermediateRAG and search4.7(3)56 lessonsSampleFumiko Arai$8 Chunking Documents for Retrieval

Chunking Documents for Retrieval

Split documents into retrievable chunks that keep their meaning, then tune chunk size with real measurements.IntermediateRAG and search4.5(2)41 lessonsSampleEmeka Nwosu$6 When Retrieval Is the Wrong Tool

When 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.All levelsRAG and search4.5(2)40 lessonsSampleFumiko Arai$5 Citations and Grounded Answers

Citations and Grounded Answers

Make RAG answers cite the right sources and check that every claim is actually supported by them.IntermediateRAG and search4.5(2)23 lessonsSampleFumiko Arai$7 Questions over Tables and Databases

Questions over Tables and Databases

Answer questions over spreadsheets, document tables and databases correctly, with text to SQL done safely.IntermediateRAG and searchNewFumiko Arai$8 Keyword Search and Inverted Indexes

Keyword Search and Inverted Indexes

Understand how keyword search really works, from inverted indexes to BM25, and tune it for your content.IntermediateRAG and searchNewEmeka Nwosu$6 Hybrid Search: Keywords Plus Vectors

Hybrid Search: Keywords Plus Vectors

Combine keyword and vector search so you find both exact codes and loosely worded questions.IntermediateRAG and searchNewEmeka Nwosu$8 Long Context Versus Retrieval

Long Context Versus Retrieval

Choose between sending whole documents to a long context model and retrieving passages, using evidence.All levelsRAG and searchNewFumiko Arai$6