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AI Fluency Curriculum

L1: Casual Consumer

L2: Prompt Engineer

L3: Context Engineer

3.1 Embeddings and Vector Space 3.2 Vector Databases 3.3 The RAG Pipeline 3.4 Chunking and Retrieval 3.5 Grounding for Accuracy 3.6 Model Architecture Awareness 3.7 Model Economics and ROI 3.8 Context Compression 3.9 Level 3 Checkpoint

L4: AI Component Engineer

L5: AI System Engineer

L6: AI Platformizer

Curriculum/Context Engineer/3.2 Vector Databases

3.2 Vector Databases

Learn how vector databases store embeddings and perform millisecond-speed similarity searches using algorithms like HNSW.

3.1 Embeddings and Vector Space 3.3 The RAG Pipeline

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Evidence-based learning for the age of AI