You're Thinking About 'Human in the Loop' All Wrong
You’re Thinking About ‘Human in the Loop’ All Wrong
"Human in the loop" is an essential concept in AI — spanning design, training, and inferencing. It’s a term that frequently comes up in our conversations with customers and investors, and we often reference it ourselves when discussing how we ensure data quality and accuracy.
But recently, we noticed a common misunderstanding. Many interpreted the phrase as meaning that humans are deeply involved in our data gathering, curation, and model training processes — in other words, that it is largely a training-focused function. That’s not what we mean at all — nor is it our design philosophy.
Continuous Learning and Feedback
Of course, humans play a role in AI training; that’s table stakes. But we don’t see that as the most critical or scalable point of engagement between humans and AI. In our view, the greatest value of "human in the loop" comes post-training — during inferencing — where feedback drives continuous learning and model improvement. Just as teams rely on real-time feedback to refine their performance, AI systems should do the same.
That’s why we’ve built extensive real-time, in-production feedback mechanisms into Charli. These loops continuously inform and enhance how the AI performs — not just how it was trained. It’s a more dynamic, scalable, and sustainable approach for ensuring accuracy and value.
Our feedback loops take many forms — from intelligent clarifications, where the AI proactively seeks guidance when data appears inconsistent, to lightweight yet powerful signals like thumbs-up or thumbs-down. These interactions are more than surface-level; they provide critical inputs that refine the AI’s output in real time. This dynamic process is enabled by our AI Hotlink building blocks and core Agentic AI workflows, which form the architectural backbone of Charli’s adaptive learning capabilities. These components are now being rapidly extended across the platform to support even broader feedback integration.
Foundational Design
In a landscape overwhelmed by fragmented and noisy data, inferencing-based feedback loops are not just a feature — they are essential infrastructure. For AI systems to remain accurate, adaptive, and trustworthy, these loops must be intentionally designed into every layer of the application stack. In today’s world of intelligent systems, continuous evolution is no longer optional — it’s foundational.
As a side note … there’s another feedback related topic on handling early warning with AI issues such as data drift, concept drift and even model collapse.