Chunking Strategies for Optimizing Large Language Models (LLMs)

Large language models (LLMs) (opens new window)have transformed the natural language processing (NLP) (new window)domain by generating human-like text, answering complex questions, and analyzing large amounts of information with impressive accuracy. Their ability to process diverse queries and produce detailed responses makes them invaluable across many fields, from customer service to medical research. However, as LLMs scale to handle more data, they encounter challenges in managing long documents and retrieving only the most relevant information efficiently.

Although LLMs are good at processing and generating human-like text, they have a limited "context window." This means they can only keep a certain amount of information in memory at one time, which makes it hard to manage very long documents. It's also challenging for LLMs to quickly find the most relevant information from large datasets. On top of this, LLMs are trained on fixed data, so they can become outdated as new information appears. To stay accurate and useful, they need regular updates.