Build pipelines for software projects can be sluggish, limiting how quickly engineers can receive feedback on their code. At Slack, builds were taking 60 minutes, hindering agility. The engineering team addressed this by integrating Bazel, a high-performance build tool, with established software principles. They modeled their build process as a directed acyclic graph, identifying dependencies between source files and deployable artifacts. This structure allowed them to optimize build performance through caching and parallel processing. Caching reduces redundant work by storing results of expensive computations. For example, using Python, the factorial function can be cached to avoid recalculating values for the same inputs. The article illustrates how effective caching requires functions to be hermetic (dependent solely on their inputs) and idempotent (producing consistent outputs). If the inputs change, the cache misses, leading to inefficiency. By refining the granularity of cache keys, such as breaking down image processing tasks into smaller units, the team improved cache hit rates and overall performance. Parallel processing also plays a critical role in enhancing build efficiency. By distributing tasks across multiple CPU cores, engineers can significantly reduce the time required for operations like image processing. For this to work, inputs and outputs must be well-defined, and the system needs to handle potential issues, such as tasks finishing out of order. The article emphasizes that the size and number of tasks should be balanced to maximize resource utilization. In Bazel, build targets are defined with specific inputs, outputs, and transformation commands, allowing for streamlined and efficient builds that leverage these software engineering techniques.