Thank you for your comment. The sliding window approach allows me to dynamically identify relevant "large chunks," which can be thought of as sections in a document. Often, your questions may pertain to multiple such sections. Using only medium chunks for retrieval could result in sparse or fragmented information.

The third retrieval focuses on "medium chunks" within these identified large chunks. This ensures that only the most relevant information is passed to the Language Model, enhancing both time efficiency and focus. For example, if you're asking for a paper summary, I can zero in on medium chunks within the Abstract, Introduction, and Conclusion sections, eliminating noise from other irrelevant sections. Additionally, this strategy helps manage token limitations, like GPT-3.5's 4000-token cap, by selectively retrieving information

Ah I see! So, the large/sliding window chunks act as a pre-filter for the medium chunks. That makes a lot of sense. I appreciate the response