Reflection tuning is a technique that enables AI models to critique and rewrite their own responses before delivering them to users, resulting in improved accuracy and reduced hallucinations. This approach involves creating a feedback loop where the model reviews its work, identifies problems, rewrites its response, and learns from the better version. While reflection tuning doubles computational costs due to multiple forward passes, it has been shown to achieve measurable benchmark improvements, with models like Llama 3.1 70B demonstrating substantial gains. To implement reflection tuning effectively, teams must prepare their training data, adapt their inference system, and instrument each stage of the process, as well as consider factors such as latency, cost, and user expectations. By weighing these trade-offs and applying reflection tuning selectively, organizations can enhance reasoning quality where it matters most while maintaining efficiency elsewhere. Ultimately, the success of reflection tuning depends on careful measurement and evaluation of its effectiveness, which can be achieved through metrics such as correction effectiveness scores, hallucination reduction, and user satisfaction trends.