Built an AI learning tool, but YouTube summarization/workflow is still our biggest challenge would genuinely love technical feedback
We’ve been building an AI-powered learning/study tool using Claude focused on turning content into:
- Summaries
- Notes
- Flashcards
- Quizzes
- Glossaries
Current formats:
✅ PDFs
✅ Websites
✅ Images / notes
Main problem:
👉 YouTube is still our biggest technical gap.
The challenges we’re running into:
- Long lecture handling
- Better transcript/context retention
- Accuracy across long-form educational videos
- Chunking without losing important concepts
- Maintaining useful flashcards/glossary quality from video content
- Cost efficiency while keeping output quality high
We’ve tested multiple approaches, but YouTube feels significantly harder than PDFs/websites because of transcript inconsistency + context loss.
Would really appreciate insight from others building with Claude or similar LLM workflows:
- Best strategies for long YouTube transcript chunking?
- How are you handling transcript cleaning + structure?
- Better methods for concept retention across long videos?
- Prompt engineering ideas for study-quality outputs instead of shallow summaries?
- Cost/performance balance suggestions?
We’re less focused on “quick summaries” and more focused on:
Helping users actually learn faster.
If anyone here has tackled similar workflows, architecture ideas or lessons would genuinely help a lot.