Understanding LLM Context Windows and Effective Prompting

This article explains the concept of context windows in large language models (LLMs) and how to work within their constraints. It discusses the importance of context size, chunking long documents, and using retrieval-augmented generation (RAG) to improve LLM performance.

Details

Large language models (LLMs) like GPT-4, Claude, and Gemini have a fundamental constraint in the form of context windows. The context window is the total amount of text the model can 'see' at once, including both the input prompt and the generated output. Anything outside this window is invisible to the model. Current context window limits range from 128K tokens for GPT-4 to 1M tokens for Gemini 1.5 Pro, with 1 token roughly equivalent to 0.75 words in English. The size of the context window is critical - too small and the model may lack the necessary information to produce coherent and consistent outputs, while too large increases the computational cost linearly. To work within these constraints, techniques like chunking long documents into smaller pieces can be employed. By splitting text into manageable chunks, the model can process the information in a more efficient manner. Another approach is retrieval-augmented generation (RAG), where the model retrieves only the most relevant information from a knowledge base to include in the context, rather than trying to stuff everything in. This can lead to better performance and more focused outputs.