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Lesson

Hybrid search and reranking

Semantic search finds similar ideas; keyword search finds names, codes, dates, and exact terms. Good RAG uses both and reorders results before responding.

  • Understand when purely vector search fails.
  • Combine dense search, sparse search, and filters.
  • Use reranking to improve the final chunks.

Recommended pipeline

Terminal
query
  -> optional rewriting
  -> permission filters
  -> dense retrieval
  -> sparse retrieval
  -> result fusion
  -> reranking
  -> top chunks with citations
  -> generation

Signals for hybrid search

  • Users ask about codes, IDs, or exact clauses.
  • There is a lot of internal terminology.
  • Documents mix tables, text, and references.
  • Semantic search returns "almost correct" answers.
Embeddings and Vector DatabasesQdrant Multi-User and Permissions
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How all courses connect

This is not a checklist. Start with the foundation, choose an outcome, and go deeper only when your project needs more control.

  1. 1Understand
  2. 2Apply or build
  3. 3Operate with confidence
01

Choose an application

Turn the foundation into a visible outcome: a website, a business improvement, media, or an interactive experience.

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Continue into the technical branch when you need to maintain code, data, or infrastructure.

02

Build with code

Prepare your environment, work with coding agents, and run models while keeping control of your projects.

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This branch prepares you to design and operate reliable AI systems.

03

Take systems to production

Combine retrieval, agents, evaluation, security, deployment, and model adaptation when the problem requires it.

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You do not need every course: choose the component your system needs and return as it grows.

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