← Explore learning paths

For AI developers

2026 Modern AI Search & RAG Roadmap

Explore retrieval, search quality, grounded generation, and the evaluation of practical RAG systems.

38 lessons ready to readFree to readLearn at your own pace
Read the first lesson → My progress in this path →
01 / READ

Start with a lesson that meets you where you are.

02 / PRACTICE

Try questions to check what you understand.

03 / RETURN

Sign in to keep your practice progress together.

Your learning path

38 of 50 topics have lessons available today. Choose any available lesson.

  1. 01

    Foundations of Modern AI Search

    Read the lesson, then try the practice.

  2. 02

    Semantic Search Principles

    Read the lesson, then try the practice.

  3. 03

    Vector Embeddings

    Read the lesson, then try the practice.

  4. 04

    Cosine Similarity & Distance Metrics

    Read the lesson, then try the practice.

  5. 05

    Vector Database Architecture

    Read the lesson, then try the practice.

  6. 06

    ANN Algorithms

    Read the lesson, then try the practice.

  7. 07

    Vector DB Selection

    Read the lesson, then try the practice.

  8. 08

    Hybrid Retrieval Systems

    Read the lesson, then try the practice.

  9. 09

    Sparse vs Dense Retrieval

    Read the lesson, then try the practice.

  10. 10

    Metadata & Filtering

    Read the lesson, then try the practice.

  11. 11

    RAG Architecture & Implementation

    Read the lesson, then try the practice.

  12. 12

    Classic RAG Pipeline

    Read the lesson, then try the practice.

  13. 13

    Context Augmentation

    Read the lesson, then try the practice.

  14. 14

    Agentic RAG Systems

    Read the lesson, then try the practice.

  15. 15

    Query Decomposition

    Read the lesson, then try the practice.

  16. 16

    Data Pipeline & Indexing

    Read the lesson, then try the practice.

  17. 17

    Document Processing

    Read the lesson, then try the practice.

  18. 18

    Smart Chunking

    Read the lesson, then try the practice.

  19. 19

    Embedding Pipeline

    Read the lesson, then try the practice.

  20. 20

    Evaluation & Quality Metrics

    Read the lesson, then try the practice.

  21. 21

    Retrieval Metrics

    Read the lesson, then try the practice.

  22. 22

    Ranking Quality

    Read the lesson, then try the practice.

  23. 23

    Generation Quality

    Read the lesson, then try the practice.

  24. 24

    Faithfulness Testing

    Read the lesson, then try the practice.

  25. 25

    Citation Coverage

    Read the lesson, then try the practice.

  26. 26

    Production Deployment & Optimization

    Read the lesson, then try the practice.

  27. 27

    Performance Optimization

    Read the lesson, then try the practice.

  28. 28

    Caching Strategies

    Read the lesson, then try the practice.

  29. 29

    Infrastructure & Security

    Read the lesson, then try the practice.

  30. 30

    Query Understanding & Intent

    Read the lesson, then try the practice.

  31. 31

    Reranking Models

    Read the lesson, then try the practice.

  32. 32

    Grounding & Hallucination Control

    Read the lesson, then try the practice.

  33. 33

    Data Freshness & Lifecycle

    Read the lesson, then try the practice.

  34. 34

    Qdrant

    Read the lesson, then try the practice.

  35. 35

    Advanced Qdrant

    Read the lesson, then try the practice.

  36. 36

    Vectorless RAG

    Read the lesson, then try the practice.

  37. 37

    Graph Rag

    Read the lesson, then try the practice.

  38. 38

    Graph RAG with Neo4J

    Read the lesson, then try the practice.

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