May 06, 2026
Modern LLMs offer huge context windows, but stuffing them full of data isn't always optimal. In fact, "lost in the middle" phenomena show that models perform best when critical information is placed at the very beginning or the very end of the prompt.
Instead of passing raw, unprocessed documents, perform initial summarization or extraction to identify the most relevant chunks. By providing the model with a dense "summary of summaries," you can pack significantly more semantic meaning into the same token budget, leading to higher-quality responses.
If your context window often contains repetitive system instructions or static reference material, use prompt caching (provided by many modern LLM APIs) to avoid the latency and cost of re-processing those tokens for every single turn in a conversation.