Sidekick's continual learning loop (2026) - Shopify

This post details the implementation of a continual learning loop ('flywheel') for AI models, focusing on compressing production experience into model weights. Key technical contributions include: defining quality via rubrics and judges, calibrating judges with DSPy and reflection-based optimizers (GEPA, ACE), using autoresearch for prompt/tool definition optimization, and employing reinforcement learning (GRPO) with hard negative mining for parameter updates. It also introduces 'gist compression' for prompt optimization. The GraphQL agent is presented as a production example demonstrating significant cost and latency improvements.