consolidate-transcripts

Consolidating YouTube transcripts for LLM analysis requires merging individual video files into a single markdown document while tracking token counts. This process ensures all relevant text fits within model context windows by stopping inclusion once a maximum token threshold is reached.

Preparing multiple YouTube video transcripts for Claude involves combining them into one file sorted by date. Using tiktoken for accurate GPT-4 and Claude token counting, the process caps total tokens at a configurable limit like 800K to prevent context window overflow.

Merging YouTube transcripts sorted by date into a single markdown file involves reading transcripts from a specified channel directory, ordering them newest first, and concatenating the text. This chronological organization ensures the most recent content is prioritized for analysis.

Tiktoken works for counting tokens when consolidating transcripts for Claude models. It provides accurate token estimation compatible with GPT-4 and Claude, allowing the consolidation process to intelligently include transcripts up to a strict token limit without exceeding model capabilities.

LLM context windows overflow when analyzing YouTube channel transcripts because raw combined text often exceeds model token capacities. Consolidating transcripts with intelligent token limit management and tiktoken counting prevents this by truncating the output before it breaches the maximum allowed context.

You can limit consolidated YouTube transcripts to a specific token count for LLM prompting by setting a configurable maximum token threshold. The consolidation process evaluates each transcript's token size and stops adding content once the desired limit is reached.