How we make AI coding more cost efficient without sacrificing task quality

This post details four specific optimizations implemented in GitHub Copilot to improve cost efficiency: 1. Selective output compaction to preserve useful context while reducing repetitive noise in build/test/lint output, while keeping source-like output unchanged. 2. Removal of line-number prefixes from the `view` tool's output, reducing model-inference cost by ~5% in benchmarks and ~3% in online experiments. 3. Prompt compression for the `task`-tool, reducing prompt tokens by ~1300 per turn through meta-prompting and a refined parallelism instruction. 4. Batching completion notifications to deliver background work results directly, eliminating an extra retrieval turn for the agent. These changes were validated through offline benchmarks and controlled online experiments.