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Design RAG Pipeline for Video Content

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Agentic Video Scene Skipper

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Linked challenge
Agentic Video Scene Skipper

Prompt source

Original prompt text with formatting preserved for inspection.

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Design a LlamaIndex-based RAG pipeline to ingest and index video content metadata, transcripts, and scene descriptions. Consider strategies for chunking, embedding, and hybrid retrieval (vector and keyword search) to optimize for natural language scene queries. Explain how you will handle different types of queries (e.g., character names, quotes, descriptive actions).

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.

Tune next

Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.

Verify after

Check whether the prompt asks for the right evidence, confidence signal, and escalation path.