How to improve context window utilization
Improving context window utilization means effectively leveraging the available input capacity of a language model to maximize performance on complex tasks. It is achievable through strategic structuring of prompts and input data.
Focus on providing the most relevant information within the token limit. Employ techniques like semantic chunking and summarization to condense key details. Structure prompts clearly and prioritize essential context. Tailor the input format to the model's strengths. Filter out redundant or irrelevant information to maintain quality and precision.
Implement this by first optimizing prompts for relevance, placing critical instructions upfront. Use hierarchical or layered approaches: break down tasks, summarize intermediate outputs, and provide these summaries as subsequent context. Continuously evaluate the information-to-noise ratio within the allocated tokens to maximize information density. This enhances reasoning over long documents and complex queries without exceeding model limits.
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