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Using Loop in AI product development - Braintrust This guide shows how you can use Loop to build, evaluate, and improve a weather agent, demonstrating how Loop can make common AI development tasks easier and more accessible
The Signals Loop: Fine-tuning for world-class AI apps and agents First-party products like Dragon Copilot and GitHub Copilot exemplify how signals loops can drive rapid product improvement, increased relevance, and long-term user engagement Dragon Copilot is a healthcare Copilot that helps doctors become more productive and deliver better patient care
Turn production data into better AI with Loop - Blog - Braintrust Loop makes production data accessible to everyone on your AI team For product managers: Analyze production behavior without writing code Ask Loop to surface patterns, identify high-impact issues, and generate insights and charts you can share directly with stakeholders
How Retool uses Loop to turn logs into AI roadmap decisions - Blog . . . By using Loop to query production data semantically and treating logs as their primary source of roadmap insights, Retool has built a process that surfaces the highest-impact work, prevents regressions, and ensures engineering effort aligns with actual user needs
The New AI Development Workflow: Building Blocks for a Better Product . . . Learn how leading AI teams run a continuous feedback loop for their generative AI products and agents When you’re building products around generative AI models, nearly everything changes about how you build, and how you operate software teams
Why Loop Marketing Matters for AI Answer Engines Loop Marketing is a continuous inbound framework built for the AI era Instead of stopping at conversion, it keeps your strategy evolving through constant testing, personalization, and optimization This matters because AI engines now shape how people find and trust brands
Feedback Loop | Definition and Overview | Product Talk This concept is fundamental to both product discovery and AI development, as it creates a systematic way to learn and improve through rapid experimentation How do feedback loops work in AI development? The feedback loop follows a cycle: analyze, measure, improve, reanalyze
The definitive guide to AI operations teams with human in the loop . . . Learn how AI operations teams combine automation with human in the loop quality assurance to improve accuracy, reduce errors, and manage complex workflows This guide explores hybrid AI human models, lifecycle stages, key benefits, provider comparisons, and how Sourcefit supports scalable AI operations through WorkingAI and Knit