For AI developers
2026 Modern AI Search & RAG Roadmap
Explore retrieval, search quality, grounded generation, and the evaluation of practical RAG systems.
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38 of 50 topics have lessons available today. Choose any available lesson.
- 01
Foundations of Modern AI Search
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- 02
Semantic Search Principles
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- 03
Vector Embeddings
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- 04
Cosine Similarity & Distance Metrics
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- 05
Vector Database Architecture
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- 06
ANN Algorithms
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- 07
Vector DB Selection
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- 08
Hybrid Retrieval Systems
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- 09
Sparse vs Dense Retrieval
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- 10
Metadata & Filtering
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- 11
RAG Architecture & Implementation
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- 12
Classic RAG Pipeline
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- 13
Context Augmentation
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- 14
Agentic RAG Systems
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- 15
Query Decomposition
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- 16
Data Pipeline & Indexing
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- 17
Document Processing
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- 18
Smart Chunking
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- 19
Embedding Pipeline
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- 20
Evaluation & Quality Metrics
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- 21
Retrieval Metrics
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- 22
Ranking Quality
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- 23
Generation Quality
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- 24
Faithfulness Testing
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- 25
Citation Coverage
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- 26
Production Deployment & Optimization
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- 27
Performance Optimization
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- 28
Caching Strategies
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- 29
Infrastructure & Security
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- 30
Query Understanding & Intent
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- 31
Reranking Models
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- 32
Grounding & Hallucination Control
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- 33
Data Freshness & Lifecycle
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- 34
Qdrant
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- 35
Advanced Qdrant
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- 36
Vectorless RAG
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- 37
Graph Rag
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- 38
Graph RAG with Neo4J
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Topics without lessons yet
These are part of the outline. Lessons are not available for them yet.
- Query Processing
- Response Generation
- Multi-Hop Reasoning
- Multimodal Processing
- Batch Processing
- Incremental Indexing
- Precision & Recall
- Latency Optimization
- Access Control
- Observability
- Cost Management
- Advanced Qdrant