Why Markdown is the right text format for video
Auto-generated captions are flat — no chapter breaks, no speaker labels, awkward line wrapping every 30-40 characters because that's what fits on a video frame. An LLM has to re-derive structure from the prose, and on long-form content (talks, podcasts, courses, lectures) it gets that derivation wrong often enough to make detailed answers unreliable.
Markdown with [HH:MM:SS] timestamp anchors and ## Speaker or ## Chapter headings gives the model three things at once: the words, the timing, and the structure. Every modern LLM — GPT, Claude, Gemini, Llama, Mistral — was trained on enough Markdown to treat heading boundaries as semantic. Auto-captions get none of this for free.
Semantic chunking that finally works
Naive chunking on flat captions splits sentences mid-clause and joins unrelated chapters. Header-aware chunking on structured Markdown respects the video's real shape: each chunk is one chapter or one speaker turn. Embeddings encode coherent content; retrieval surfaces complete arguments instead of orphan fragments.
Model-specific guides
- ChatGPT — long talks and podcasts, custom GPT knowledge bases
- Claude — conference archives in Projects, 200K-token windows
- Gemini — controllable input vs the 1M-token native video path
- RAG — video knowledge bases for production retrieval
- LangChain and LlamaIndex — code-level integration
For other source modalities: PDF for LLMs, URL for LLMs, Audio for LLMs — same principles, different inputs.