Chunking for beginners: 3 simple techniques in RAG systems
Skills:
RAG Basics80%
Why does every RAG pipeline start with chunking? Because chunking defines what your vectors mean.
At its core, ๐ฐ๐ต๐๐ป๐ธ๐ถ๐ป๐ด is the preprocessing step of splitting texts into smaller pieces - and each chunk becomes the unit of information that gets vectorized and stored in your vector database.
In this short video, Femke breaks down simple chunking methods โ token, sentence, and document-based.
๐ย Get your copy of the free advanced RAG ebook: https://weaviate.io/ebooks/advanced-rag-techniques?utm_source=youtube&utm_campaign=rag&utm_content=680991368
Chapters:
00:00:00 - Why Large Docs Challenge AI Models
00:00:17 - Token-Chunking
00:00:29 - Sentence-Chunking for Better Context
00:00:45 - Document-Based Chunking Benefits & Limits
00:01:03 - Combining Chunking Methods
00:01:09 - Smarter Chunking Approaches
00:01:18 - Next Steps & Additional Resources
Paper review video: Late chunking improves context recall in RAG pipelines
https://www.youtube.com/watch?v=buzWGXOydD8
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Chapters (7)
Why Large Docs Challenge AI Models
0:17
Token-Chunking
0:29
Sentence-Chunking for Better Context
0:45
Document-Based Chunking Benefits & Limits
1:03
Combining Chunking Methods
1:09
Smarter Chunking Approaches
1:18
Next Steps & Additional Resources
๐
Tutor Explanation
DeepCamp AI