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## TL;DR
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PR-Agent uses an **asymmetric and dynamic context strategy** to improve AI analysis of code changes in pull requests.
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Qodo Merge uses an **asymmetric and dynamic context strategy** to improve AI analysis of code changes in pull requests.
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It provides more context before changes than after, and dynamically adjusts the context based on code structure (e.g., enclosing functions or classes).
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This approach balances providing sufficient context for accurate analysis, while avoiding needle-in-the-haystack information overload that could degrade AI performance or exceed token limits.
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- Increased context expands the token count, increasing processing time and cost, and may prevent the model from processing the entire pull request in a single pass.
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## Asymmetric and dynamic context
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To address these challenges, PR-Agent employs an **asymmetric** and **dynamic** context strategy, providing the model with more focused and relevant context information for each code change.
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To address these challenges, Qodo Merge employs an **asymmetric** and **dynamic** context strategy, providing the model with more focused and relevant context information for each code change.
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**Asymmetric:**
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We start by recognizing that the context preceding a code change is typically more crucial for understanding the modification than the context following it.
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Consequently, PR-Agent implements an asymmetric context policy, decoupling the context window into two distinct segments: one for the code before the change and another for the code after.
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Consequently, Qodo Merge implements an asymmetric context policy, decoupling the context window into two distinct segments: one for the code before the change and another for the code after.
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By independently adjusting each context window, PR-Agent can supply the model with a more tailored and pertinent context for individual code changes.
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By independently adjusting each context window, Qodo Merge can supply the model with a more tailored and pertinent context for individual code changes.
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**Dynamic:**
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