Discover the Power of Semantic Chunking for Better Results
Have you ever felt like your language model is missing the bigger picture? Neighbors could still be different, leaving gaps in understanding.
Language models have their limits, with a context window typically restricted to 128k tokens, equivalent to about 80k English words. While this may seem sufficient, large-scale applications often demand access to more extensive data beyond this limit, including images and tables.
Loading up the context window with irrelevant information can significantly impact a Language Model’s performance.
Enter RAG. RAG leverages semantic chunking to extract relevant information from a source and deliver it as context to the Language Model. By dividing documents into manageable chunks, chunking plays a crucial role in optimizing RAG pipelines.
The strategic use of semantic chunking allows RAG to retrieve specific sections of a large document, influencing the accuracy of responses generated by the Language Model.