Master RAG
engineering.
From fundamentals to cutting-edge research. Build production-ready RAG systems with interactive labs and real-world challenges.
220+
Challenges
64
Modules
15
Stages
12-Module Curriculum
Production-focused learning path from embeddings to scalable operations.
Interactive Labs
Executable environments for chunking, vector search, and reranking.
Decision Playbooks
Matrices for choosing retrieval models, LLMs, and guardrails.
Popular Challenges
Start with these community favorites
Advanced RAG Architectures
Master cutting-edge techniques from the latest research papers including MiA-RAG, QuCo-RAG, HiFi-RAG, and more.
MiA-RAG
Mindscape-Aware Context
Hierarchical summarization for global document understanding
QuCo-RAG
Corpus-Based Uncertainty
Trigger retrieval using pre-training corpus statistics
HiFi-RAG
Hierarchical Filtering
Multi-stage filtering for maximum context precision
Graph-O1
MCTS Reasoning
Monte Carlo Tree Search for graph exploration
Your Learning Journey
Progress through carefully designed stages from fundamentals to production
Foundations
4 challenges
Retrieval
6 challenges
Post-Retrieval
8 challenges
Evaluation
6 challenges
Production
5 challenges
A production RAG system, step by step.
Scroll through the pipeline to understand how each component works together.
Ingest with traceability
Parse → chunk → attach metadata → stable chunk IDs. Debuggability beats cleverness.
- Stable chunk_id + doc_id + offsets
- Structure-aware chunking (headers, tables, code)
- Dedup + versioning so re-indexing is safe
Maximize recall (hybrid + fusion)
Default production strategy: BM25 + dense embeddings fused with RRF.
- Metadata filters first (tenant/doc_type/date)
- Hybrid retrieval (BM25 + dense)
- RRF fusion to avoid score-normalization pitfalls
Maximize precision (rerank)
Rerank top‑20/50 candidates with a cross‑encoder, keep the best 5–10.
- Reranker cascade: cheap shortlist → expensive rerank
- MMR diversity to reduce redundancy
- Lost-in-the-middle ordering
Ground answers (and refuse safely)
Citations, refusal policies, injection/PII defenses — production hardening isn't optional.
- Citation validation (no out-of-range cites)
- Refuse on insufficient context
- Sanitize prompt injection + redact PII before LLM
RAG pipeline (scrollytelling)
Scroll left side — diagram reacts as pipeline moves from ingestion to retrieval, reranking, and safety.
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