Context Window

The context window is the maximum amount of text (measured in tokens) that a language model can consider at once. It includes everything — the system prompt, conversation history, knowledge base content, and the model's response. Think of it as the model's working memory.

Context window sizes have grown dramatically: GPT-3 had 4,096 tokens (~3,000 words), GPT-4 Turbo supports 128,000 tokens (~96,000 words), Claude 3.5 supports 200,000 tokens (~150,000 words), and Gemini 1.5 Pro supports up to 2,000,000 tokens.

Larger context windows enable: longer conversations without losing context, processing entire documents or codebases, including more knowledge base content for accurate answers, and handling complex multi-step tasks that require extensive context.

However, more context isn't always better. Models can experience "lost in the middle" effects where information in the middle of a long context is less likely to be recalled. Processing longer contexts is also more expensive and slower.

For AI agent builders, context window management is crucial. Every conversation turn, system prompt instruction, and knowledge retrieval result consumes tokens. Effective agents use techniques like RAG to selectively include only relevant knowledge rather than stuffing the entire knowledge base into the context.

Token optimization strategies include: concise system prompts, summarizing long conversation histories, chunking knowledge base documents, and using retrieval (RAG) to include only relevant passages.