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Retrieval-Augmented Generation (RAG)
Retrieval
Retrieval means selecting the most useful document chunks and sending them to the AI model.
This is a simplified educational demo. Real retrieval systems use more advanced ranking and filtering.
Interactive Playground
Knowledge Base
3
Live Visualization
❓
Question
→
🔍
Vector
Search
Search
→
🏆
Rank
Results
Results
→
🎯
Select
Top K
Top K
→
📬
Send to
LLM
LLM
All Chunks
Context Given to AI
Statistics
8
Total Chunks
8
Chunks Compared
3
Retrieved Chunks
3
Top K
0 words
Context Size
How It Works
❓
Question
→
🔍
Vector
Search
Search
→
🏆
Rank
Results
Results
→
🎯
Retrieve Top
Chunks
Chunks
→
📬
Send Context
to LLM
to LLM
Vector Search vs. Retrieval
This distinction is the main goal of this lesson.
8
Search Results
Vector Search finds similar chunks — it scores every chunk in the knowledge base.
→
3
Retrieved
Retrieval chooses which of those chunks are actually sent to the AI.
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Tips
3 tips
Search happens on every chunk, but retrieval only keeps the top few. The rest never reach the AI.
Try raising or lowering Top K — a bigger K sends more context, but also more noise.
The Context Given to AI box is exactly what the language model sees, nothing more.
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Key Takeaway
Vector Search finds relevant information. Retrieval decides which information the AI actually receives.