Adaptive Feedback Loop

The Problem

Generic LLM scoring models carry an implicit bias: they reflect the distribution of their training data, not your organization's specific standards. A model trained on public data doesn't know that your compliance team considers "partial disclosure" a critical failure, or that your top-performing agents use informal language that still meets all policy requirements. The result is a high false-positive rate that erodes trust in the system.

Architecture

Active Learning Pipeline

Rather than sampling interactions randomly for human review, the system implements a strategic sampling algorithm. The model surfaces only the cases where it has low confidence — edge cases, novel language patterns, and disputed scores — routing them to senior QA leads for calibration. This maximizes the information value of every human annotation.

The Correction Workbench

• A custom interface designed for QA leads to not just "reject" a score, but provide natural language justification: "This agent was compliant — the disclosure was in the opening statement, not the close."
• Justifications are structured into preference pairs (correct vs. incorrect scoring rationale) for use in Direct Preference Optimization.
• Agents can also "challenge" AI feedback, which flows into the same pipeline — creating a bidirectional calibration loop.

Incremental Fine-Tuning via LoRA

• A weekly re-training cycle converts accumulated human corrections into a LoRA (Low-Rank Adaptation) adapter update.
• The base model remains frozen; only the adapter weights shift — keeping update costs low and rollback trivial.
• DPO (Direct Preference Optimization) aligns the model's scoring behavior to the organization's expressed preferences without requiring reward model training.

Semantic Drift Detection

• Automated monitoring of embedding distributions across weekly conversation batches detects when customer language is evolving — new product terminology, slang, or regulatory vocabulary.
• Drift alerts trigger targeted re-annotation campaigns, keeping the model current within a 24-hour response window.

Results