Chunking Techniques for RAG: Optimizing LLM Responses

Описание: In this video, we will delve into the concept of Chunking.

RAG allows for the creation of text embeddings from key data, positioning them within the semantic space that LLMs use to generate responses. This ensures the AI's answers are grounded in specific information while also providing citations from original texts.

While LLM providers are expanding context windows, they often charge based on the number of input tokens. Attempting to fit large documents into these context windows can be costly and challenging, as LLMs must parse relevant information sequentially. This is where chunking comes in.

Chunking is more than just breaking down data; it requires careful consideration. The size of data chunks significantly impacts search results: too much information can dilute specificity, while too little can strip away essential context.

Let's explore more about Chunking in the video!!