Context Window Optimization: Making the Most of Limited Tokens

Context windows keep growing—Claude offers 200K tokens, GPT-4 Turbo offers 128K—but you still run out. Optimizing what goes into context is as important as having more of it.

Relevance filtering is step one. Not everything in your documents belongs in every prompt. Build systems that select relevant chunks based on the query, not just everything that might be related. RAG systems are fundamentally about smart filtering.

Compression techniques help fit more meaning in fewer tokens. Summarize background information, use abbreviations consistently, remove formatting that doesn't carry meaning. A compressed context often performs better than a verbose one at the same token count.

Structured context improves model understanding. Clear sections, consistent formatting, explicit relationships between pieces of information. The model parses structured context more reliably than unstructured dumps.