Continuous AI Learning
The controlled process of using observed performance, evaluations, and validated human corrections to improve an AI system -- not automatic training from every interaction.
Quick help: Continuous AI learning is the controlled process of using observed system performance, evaluations and validated human corrections to improve an AI system. It does not mean allowing a production model to automatically train itself from every interaction.
What It Is
AI systems generate valuable operational evidence: successes, failures, human corrections, retrieval misses, tool mistakes, workflow problems. A mature system can use this evidence to improve. The important word is controlled.
Improvement Loop
Run -> Evaluate -> Diagnose -> Correct -> Test -> Promote or Reject
This differs fundamentally from: Interaction -> Automatically train model. The second approach can reinforce errors and undesirable behavior.
Diagnose Before Learning
Not every failure requires model training.
Knowledge Failure
Add or correct knowledge.
Retrieval Failure
Improve retrieval.
Reasoning Failure
Investigate instructions/model.
Workflow Failure
Modify graph.
Tool Failure
Fix integration.
Behavioral Failure
Consider model adaptation.
Rule Failure
Fix deterministic code.
This taxonomy prevents expensive or inappropriate remedies.
Human Corrections
A correction should preserve: input, AI output, corrected output, reason, sources, model, instructions version, graph version, evaluation. This makes the correction useful beyond the immediate interaction.
Training Dataset
Validated corrections can eventually become training examples. Example lifecycle: Correction -> Review -> Validated Example -> Dataset Version -> Training Experiment. Training data should itself have provenance and quality controls.
Model Adaptation
Possible techniques include LoRA, QLoRA, and DoRA. Parameter-efficient fine-tuning methods allow a comparatively small set of parameters to be trained while keeping most or all of the base model fixed. DoRA is a LoRA variant that separates weight updates into magnitude and direction components.
Evaluation Before Promotion
A newly adapted model is a candidate, not automatically an improvement. Base Model -> Baseline Eval; Adapted Model -> Same Eval; Compare -> Promote / Reject.
Possible measurements: task accuracy, behavior consistency, tool selection, grounding, latency, memory requirements, cost.
Knowledge vs Behavior
A particularly important distinction: RAG/Wiki primarily changes what information the model can access. Fine-tuning primarily changes model behavior. Frequently changing factual information usually belongs in external knowledge rather than model weights.
Governance Considerations
Model adaptation creates a new model artifact or configuration requiring lifecycle management. Record: base model, training method, dataset, dataset version, parameters, evaluation results, approval, deployment status. Do not lose the ability to reproduce how an adapted model was created.
Practical Experiment
After KB Sandbox accumulates enough validated corrections: (1) select one repeated behavioral failure; (2) create a small validated dataset; (3) establish base-model evaluation; (4) train a LoRA/QLoRA adapter; (5) run exactly the same evaluation; (6) compare performance; (7) optionally repeat using DoRA; (8) determine whether adaptation produced meaningful improvement. The purpose is not merely to successfully train an adapter -- the purpose is to demonstrate measurable improvement.
Last Verified
August 2026