With modern LLMs offering 200k+ token windows, it is tempting to utilize the full context for every query by including comprehensive documentation. While easy to implement, this pattern has scaling implications.
The Challenge: Managing Cost and Latency at Scale.
Large context windows introduce distinct trade-offs:
- Efficiency: High token usage correlates directly with cloud costs.
- Latency: Processing massive inputs increases "Time to First Token."
- Recall: Some studies suggest that information buried in the middle of massive contexts can be retrieved less reliably.
Recommendation: Intelligent RAG (Retrieval Augmented Generation).
We recommend treating context as a scarce resource. By retrieving only the specific snippets relevant to the user's current query, you create a system that is faster, cheaper, and often more accurate.