RAG Pipeline Flow - Interactive Visualization

Retrieval-Augmented Generation (RAG) grounds LLM responses in real documents, dramatically reducing hallucination. The pipeline embeds the user query, searches a vector database for similar document chunks, ranks results by relevance score, assembles a context-rich prompt, and sends it to the LLM. Each step has tunable parameters that affect whether the model gets the right context.

• Embedding stage: see the query represented as a 20-dim vector bar chart
• Vector search: cosine similarity scores shown for each retrieved document
• Rank and filter: low-score docs grayed out and filtered below similarity threshold
• Prompt augmentation: watch the prompt assembled from system + context + query pieces
• Top-K slider: see how more retrieved docs increases recall but may add noise
• Similarity threshold: too high = miss relevant docs, too low = include irrelevant docs

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