Playground 3: RAG Pipeline Explorer

Explore how Retrieval-Augmented Generation dynamically furnishes an agent's LLM core with essential context precisely when it's needed, at the point of inference. Step through the pipeline to see data flow at every stage.

Mode
Parameters
256
How documents are split for embedding
3
Number of retrieved chunks
0.70
Min cosine similarity for inclusion
Presets
Pipeline Flow
Step 0 / 0
Knowledge Base Chunks
What's Happening
Click Next Step to begin stepping through the RAG pipeline. Each stage shows exactly what data flows and why it matters.
Aspect No RAG Basic RAG Agentic RAG
KnowledgeStatic pretrained data onlyRetrieved docs augment contextIteratively refined retrieval across sources
Query HandlingDirect to LLM, no preprocessingSingle embed + retrieve passQuery refinement + multi-pass search
Hallucination RiskHighMediumLow
CitationsNonePossible with source metadataBuilt-in with provenance tracking
Response QualityGeneric / may be wrongGrounded in retrieved factsSynthesized from multiple sources
Best ForGeneral chat, creative tasksFactual Q&A, customer supportComplex research, multi-step reasoning
1. No RAG at All The book's PoC failure story: "A successful PoC that works on clean data in a lab is not a guarantee of a production-ready system." The agent hallucinated a return policy that didn't exist.
2. Poor Chunking "The quality of your retrieval is paramount. Invest in a robust document processing pipeline that cleans and chunks your source data effectively." Poorly chunked docs lose context and break retrieval.
3. Ignoring "No Match" When retrieval returns nothing relevant, the agent should say "I don't know" instead of hallucinating. Book example: agent confidently told a customer they were eligible for a refund that didn't exist.
4. Static Knowledge Only RAG's power is access to CURRENT information. The book contrasts "Static, pretrained model data" vs "Dynamic, external knowledge base (service bulletins)."
5. Not Citing Sources "This often allows the agent to provide citations or references back to the source documents, which dramatically increases transparency and user trust." Always surface where retrieved info came from.
Step through the pipeline to generate the augmented prompt...