🔎Learn the Architecture of RAG Systems
Separate RAG into three pipelines — offline ingest, online retrieval, generation grounding — so each can be debugged on its own. By the end, you'll sketch a documentation-chatbot architecture and label every failure mode.
Phase 1Why Retrieval Exists and What RAG Actually Is
See why LLMs need retrieval and what RAG actually solves
RAG isn't a feature — it's three pipelines pretending to be one
6 minRAG isn't a feature — it's three pipelines pretending to be one
LLMs hit three walls — RAG only fixes two of them
6 minLLMs hit three walls — RAG only fixes two of them
Six boxes, two clocks: the only RAG diagram you need
6 minSix boxes, two clocks: the only RAG diagram you need
Index, retrieve, ground — the three verbs that name every component
6 minIndex, retrieve, ground — the three verbs that name every component
Phase 2Walking One Query End to End
Walk one query end-to-end through the full RAG pipeline
Chunking is where retrieval quality is born or buried
6 minChunking is where retrieval quality is born or buried
Embeddings: turning meaning into a number you can search
6 minEmbeddings: turning meaning into a number you can search
Vector search is the floor, not the ceiling, of retrieval
7 minVector search is the floor, not the ceiling, of retrieval
The prompt is the contract between retrieval and the model
6 minThe prompt is the contract between retrieval and the model
Trace one query through six stages and watch where time and quality go
7 minTrace one query through six stages and watch where time and quality go
Phase 3Where the Pipeline Breaks at Scale
Spot where each stage breaks once your corpus grows
Why the demo that worked on 50 docs falls apart at 50,000
6 minWhy the demo that worked on 50 docs falls apart at 50,000
Embedding drift: when the model and your corpus walk away from each other
6 minEmbedding drift: when the model and your corpus walk away from each other
Top-K is a recall knob, not a quality knob
6 minTop-K is a recall knob, not a quality knob
When grounding lies: the model invents a citation that almost matches
7 minWhen grounding lies: the model invents a citation that almost matches
Phase 4Sketch a Documentation Chatbot Architecture
Sketch a doc-chatbot architecture and label its failure modes
Sketch a documentation chatbot architecture and label every failure mode
8 minSketch a documentation chatbot architecture and label every failure mode
Frequently asked questions
- What is RAG architecture and why do LLMs need retrieval?
- This is covered in the “Learn the Architecture of RAG Systems” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- What's the difference between indexing, retrieval, and grounding in RAG?
- This is covered in the “Learn the Architecture of RAG Systems” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- Why does my RAG demo get worse as I add more documents?
- This is covered in the “Learn the Architecture of RAG Systems” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- How do top-k, reranking, and chunk size interact?
- This is covered in the “Learn the Architecture of RAG Systems” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- Where do most production RAG systems actually fail?
- This is covered in the “Learn the Architecture of RAG Systems” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
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