Semantic search explained: why keyword search fails for interview archives

Retrieval-augmented generation answers questions about indexed interview transcripts by retrieving relevant chunks and grounding the model's output in those chunks. Here is how the pipeline works and where it still fails.

A grounded language model is constrained to produce output traceable to specific source chunks. Here is what grounding means in practice, why it produces usable output, and where the model can still hallucinate.

Transcript-based editing means selecting interview clips by reading the words instead of scrubbing through audio. Here is why it dominates modern documentary post and how the workflow runs.