How to Chunk Video for RAG: Pause‑Based, Overlap Windows, and LLM‑Driven Topic Segmentation

When building a Retrieval‑Augmented Generation (RAG) system for video, the lack of inherent textual structure makes simple paragraph or token‑based chunking ineffective. The article starts by highlighting this problem and defines chunking as the process of splitting large information into meaningful fragments for LLMs or vector databases.

Pause‑Based Chunking

The first practical solution is pause‑based chunking. By assuming the transcript includes start‑ and end‑time stamps for each sentence or utterance, the algorithm compares the gap between the previous segment’s end time and the next segment’s start time. Natural pauses that occur at speaker changes, slide transitions, or topic shifts become chunk boundaries. An example shows a transcript about CI/CD being split into two chunks, where the first chunk ends with “CI/CD 把……的过程自动化”. The article points out that relying solely on pauses can break context, e.g., a short breath while explaining a complex concept may produce an incomplete sentence.

Overlapping Window and Length‑Based Fallback

To preserve context, an overlapping window (e.g., 5 seconds or a few sentences) is added between adjacent chunks, ensuring that neighboring segments share some content. When videos are fast‑paced and contain few pauses, pause‑based chunking fails. In such cases the method falls back to a recursive length‑based strategy: if a segment exceeds a maximum length (e.g., 200 words) and lacks a pause, it is split at sentence boundaries.

LLM‑Based Topic Chunking

For higher‑level queries like “What is the overall topic of this video?”, the article proposes LLM‑based topic chunking. Fine‑grained chunks are fed to an LLM together with a prompt that asks the model to cluster and summarize them, producing metadata such as a topic label, summary, start/end timestamps, and key terms. The article includes a concrete JSON example:

{
  "topic": "Introduction to CI/CD Fundamentals",
  "summary": "Covers the basic definition of CI/CD, its role in modern deployment, and the foundational stages of a build pipeline.",
  "start": 0,
  "end": 120,
  "key_terms": ["CI/CD", "deployment", "build stage"]
}

Combining Fine‑Grained and Topic Chunking

A production‑grade RAG pipeline uses both strategies. Fine‑grained chunks are stored in a vector database for precise retrieval of timestamps and exact answers. Topic chunks are stored for global retrieval and summarization tasks. The article concludes with a diagram (illustrated by an image) that shows the end‑to‑end flow: raw video → transcription with timestamps → pause‑based chunking + overlap → fallback length‑based split → LLM‑driven topic generation → dual storage → query handling.

Overall, the way data is chunked determines how well the retrieval system can understand and answer questions, and moving from uniform splits to pause‑aware and LLM‑driven multi‑layer chunking enables agents to handle both specific technical queries and broad thematic questions effectively.