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Example: Google’s internal experience showcases how consistent AI integration has led to a 45% increase in code coverage and a 60% reduction in mean time to resolution (MTTR).
Infrastructure and Model Strategies
To effectively embed AI into software workflows, enterprises must select models and infrastructures that address critical parameters:
Latency and Throughput: Prioritizing models capable of processing 300+ tokens per second while maintaining high accuracy.
Model Size and Context Window: Utilizing models like Gemini's Flash or Pro, which support 1-2 million tokens in context, to handle vast codebases or documentation. This extension dramatically benefits tasks like large-scale code refactoring or complex data analysis.