Text Splitters & Embeddings

When working with large documents, directly feeding them to a Large Language Model (LLM) often causes problems. LLMs have strict input limits, known as context windows.

This lesson teaches you how to prepare large texts for LLMs using two key techniques: text splitting and embeddings. These are essential for building advanced AI agents.

Imagine you have a 100-page PDF document. If you try to ask an LLM a question about it, you can't just send the whole document.

• Context Window Limits: LLMs can only process a certain amount of text at once (e.g., 4,000 to 128,000 tokens).
• Cost: Longer inputs mean higher API costs.
• Relevance: Filling the context window with irrelevant information can make the LLM 'forget' the important parts.

Text splitting solves this by breaking documents into smaller, manageable chunks.