⚠️ Retrieval-Augmented Generation Medical Accuracy Simulator

The Clinical Query Enters the Retrieval Pipeline

Placeholder lead: a natural-language clinical question kicks off retrieval.

• Free text: Query type (Placeholder caption)
• Dense vector: Encoding (Placeholder caption)
• <300 ms: Latency budget (Placeholder caption)
• Corpus search: Downstream step (Placeholder caption)

Placeholder section heading

Placeholder body text, short filler describing query embedding and intent.

Searching the Unstructured Document Corpus

Placeholder lead: the retriever scans literature for relevant passages.

• ~2.4M docs: Corpus size (Placeholder caption)
• ~300 tokens: Chunk size (Placeholder caption)
• Hybrid dense+sparse: Search type (Placeholder caption)
• ~200: Candidates pulled (Placeholder caption)

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Placeholder body text about vector search over corpus chunks.

Relevance Scoring and Top-K Passage Selection

Placeholder lead: passages ranked, only the best K move forward.

• Cross-encoder: Reranker (Placeholder caption)
• 5–10: Typical K (Placeholder caption)
• 0–1: Score range (Placeholder caption)
• ~0.6: Cutoff threshold (Placeholder caption)

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Placeholder body text about reranking and threshold cutoffs.

The LLM Generates an Answer Grounded in Retrieved Text

Placeholder lead: model composes answer citing retrieved passages only.

• ~4k tokens: Context window used (Placeholder caption)
• Inline refs: Citation style (Placeholder caption)
• Parametric memory: Fallback (Placeholder caption)
• Post-hoc verify: Grounding check (Placeholder caption)

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Placeholder body text about prompt construction and grounding.

Answer Accuracy Tracks Retrieval Quality, Not Architecture Alone

Placeholder lead: good retrieval yields grounded answers, poor retrieval can still hallucinate.

• ~90%: High-quality retrieval accuracy (Placeholder caption)
• ~40%: Poor retrieval accuracy (Placeholder caption)
• Grounded hallucination: Failure mode (Placeholder caption)
• Corpus curation: Mitigation (Placeholder caption)

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Placeholder body text about retrieval quality driving final accuracy.

Placeholder highlight: RAG only grounds on what was actually retrieved.