There's an enormous amount of technical knowledge that exists only as video — conference talks, deep-dive tutorials, recorded workshops. If you're building anything RAG-shaped, that content is invisible to your pipeline until you turn it into text. Transcripts are how video becomes retrievable, but the naive approach — dump raw captions into your embedder — fails in predictable ways.
Why raw captions break retrieval
- Caption files are timestamped fragments a few words long. Embed those and every vector is context-free noise.
- Auto-captions repeat lines at fragment boundaries — dedupe or your index is full of near-identical chunks.
- Timestamps and markup burn tokens and pollute embeddings without adding retrievable meaning.
- Naive fixed-size re-chunking cuts mid-sentence, so retrieved passages arrive decapitated.
The pipeline that works
First normalize: merge fragments into paragraphs of clean Markdown, stripped of timing data. Then treat the transcript like any other document in your RAG pipeline — structure-aware chunking, embeddings, hybrid retrieval with reranking. The transcript-specific work is all in that first step; everything after is standard.
Doing it in one call
Kit for AI collapses the pipeline into an ingest: pass a YouTube URL to the API (or paste it in the dashboard, or let your agent call the MCP tool), and the transcript is fetched, cleaned to Markdown, chunked on structure, embedded, and indexed into a knowledge base. Queries against that KB run hybrid semantic-plus-keyword search with reranking and return cited passages — ready to drop into a model's context.
Mixing video with everything else
The quiet advantage of normalizing to Markdown: transcripts stop being a special case. A knowledge base can hold the talk, the speaker's slides (as PDF), and the project's docs (as URLs) side by side, and one query retrieves across all three. Your agent doesn't care that a passage started life as speech.
Limits worth designing around
Caption availability is the gate — no captions, no ingest, and your pipeline should surface that as a clear error rather than an empty document. Auto-caption quality on jargon-heavy content is the other: retrieval still works because embeddings are tolerant of small transcription errors, but quoted passages may need a human glance before they go anywhere customer-facing.
The API and MCP setup are covered in the docs — free tier included, so you can test with a real talk today.