Unverified50% confidenceFactExact time
One of the core challenges of the harness is context window management, as real-world codebases can easily run to millions of tokens, far exceeding the context length limits of current models.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
9/17/2026
First Seen
Valid until: 12/16/2026
Sources
Related Entities
Related Claims
UnverifiedThe size of the context window directly determines how much code context the model can see, and is the core bottleneck affecting the quality of code comprehension in large projects.71% similarUnverifiedIn large model usage, the context window is a limited resource, and token consumption accumulates with conversation turns, file reads, and code injection.70% similarUnverifiedThe RAG architecture can circumvent the context window length limitation of LLMs by retrieving the most relevant code snippets to inject into the prompt rather than loading the entire codebase.67% similarUnverified在大模型领域，Harness 层承担 prompt 管理、上下文窗口调度、工具调用协议、错误重试、token 预算控制等职责66% similarUnverifiedAPI calls to large models are typically billed by the number of Tokens, and each model has a Token count limit for its context window66% similar
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