01
AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
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Five outcome-based courses
Each course includes explicit outcomes, mapped competencies, practice activities, and an applied capstone.
02
Responsible AI User
Use AI productively while protecting privacy, checking outputs, and preserving human accountability.
03
AI at Work
Evaluate workplace use cases, run safe pilots, measure value, and communicate changes responsibly.
04
AI Policy & Society
Analyze AI systems through rights, equity, governance, safety, and public-interest outcomes.
05
Building with AI Systems
Understand language models, retrieval, agents, evaluation, cost, and deployment safeguards through practical system design.
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FundamentalsCore concepts: how AI works at a high level.84 guides Language AILLMs, NLP, ChatGPT, prompting, and text understanding.155 guides Visual AIImage generation, computer vision, video, and design.133 guides Audio AIVoice, music, podcasting, and audio-first AI.117 guides CompaniesProfiles of OpenAI, Anthropic, Google, Meta, and more.121 guides ApplicationsHow AI shows up in coding, search, agents, and daily life.111 guides IndustriesAI in healthcare, finance, education, energy, and more.91 guides TechnicalChips, fine-tuning, RAG, vectors, and AI infrastructure.177 guides SocietyBias, privacy, copyright, safety, and the AI future.30 guides
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Language AI
ColBERT Late Interaction Retrieval
ColBERT is a retrieval model that represents each query and document as many token-level vectors and scores them with a fine-grained 'late interaction' step.
Language AIMaximum Marginal Relevance
Maximum Marginal Relevance (MMR) is a re-ranking method that balances how relevant a result is against how different it is from results already chosen.
Language AISelf-RAG and Reflective Retrieval
Self-RAG is a framework where a language model decides when to retrieve, then critiques both the retrieved passages and its own output using special…
Language AIGraphRAG Knowledge Graphs
GraphRAG enhances retrieval-augmented generation by building a knowledge graph of entities and relationships from a document collection, then retrieving over…
Language AISkeleton-of-Thought Parallel Decoding
Skeleton-of-Thought (SoT) is a prompting and decoding technique that first asks a language model to outline a brief skeleton of answer points, then expands…
Language AIChain-of-Verification for Hallucination Reduction
Chain-of-Verification (CoVe) is a prompting method where a model drafts an answer, generates its own fact-checking questions, answers them independently…
Language AIEntity Linking and Disambiguation
Entity linking maps mentions of names in text to unique entries in a knowledge base, deciding, for example, whether 'Paris' means the city or the person.
Language AILogit Lens and Intermediate Layer Decoding
The logit lens is an interpretability trick that decodes a transformer's hidden states at every layer into vocabulary predictions, letting you watch a guess…
Language AIInduction Heads in Transformers
Induction heads are attention heads that implement a simple but powerful copy rule: 'I saw [A][B] earlier, and now I see [A] again, so predict [B].
Language AIPosition Interpolation for Context Extension
Position Interpolation (PI) is a technique that stretches a language model's usable context window far beyond its training length by rescaling positional…
Language AIYaRN Context Window Scaling
YaRN (Yet another RoPE extensioN) is a technique that stretches a transformer's usable context window far beyond what it was trained on, with minimal…
Language AIJamba Hybrid Transformer-Mamba Models
Jamba is a large language model from AI21 Labs that interleaves Transformer attention layers with Mamba state-space layers (plus mixture-of-experts) to get…
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