Production RAG & GraphRAG
Chunking, query transformation, hybrid retrieval, reranking, entity resolution, and global graph search.
Concept overview
What this lesson will help you understand.
Production retrieval requires document structure, multi-stage ranking, graph construction, evaluation, authorization, and operational lineage to work together.
Learn by doing
Explore the concept.
Make a prediction before changing a control. Run the experiment, explain what moved, and compare the result with the theory below.
Initializing Interactive Playground...
Complete lesson
Detailed concept deep dive.
Build intuition first, then work through implementation, mathematical derivations, failure analysis, and real system decisions.
8 guided chapters
Production retrieval requires document structure, multi-stage ranking, graph construction, evaluation, authorization, and operational lineage to work together.
Before working through the formal derivations, connect the vocabulary to one small, concrete example. The goal is not to memorize definitions in isolation. It is to understand what each idea represents, which assumptions make it valid, and how the pieces relate to one another.
Ideas to understand
- Layout-aware parsing and metadata
- Recursive, parent-document, sentence-window, and proposition chunking
- Dense, sparse, hybrid, and reranked retrieval
Learn by doing
Make the idea concrete
Explain the core model and vocabulary for Production RAG and GraphRAG using a concrete example, measurements, and a short design explanation.
Try this
- Define “Layout-aware parsing and metadata” in your own words, then annotate one concrete Production RAG & GraphRAG input and output.
- Construct one valid case and one counterexample for “Recursive, parent-document, sentence-window, and proposition chunking”; explain which assumption separates them.
- Predict how “Dense, sparse, hybrid, and reranked retrieval” will change one visible playground result, then test and record the before/after values.
Evidence of understanding
- You can explain the example without relying on jargon.
- You can name the assumptions and identify what would invalidate them.
- You can connect the example to at least one real ML use case.
What part of the Production RAG & GraphRAG mental model still feels least intuitive, and what example would help clarify it?
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Keep learning
Papers, standards, and practical references.
Start with the free primary sources. Books are included where a longer, connected treatment is worth the investment.
Microsoft GraphRAG
The reference implementation and documentation for graph-based indexing and local/global search.
From Local to Global: A GraphRAG Approach
The research paper describing entity graphs, community summaries, and global corpus questions.
Precise Zero-Shot Dense Retrieval with HyDE
The paper introducing hypothetical document embeddings for query-document alignment.
ColBERTv2
Late-interaction retrieval with token-level matching and efficient residual compression.
Neo4j Cypher Manual
A practical graph-query reference for entity, path, and pattern retrieval.
Test your understanding.
Answer explanations appear after every choice. Missed questions can be reviewed before a full retake.
Mastery requires 80% or higher.
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