RESEARCH / ENTERPRISE AI
Proposes a governed learning loop that turns user corrections and usage signals into improvements across knowledge, search, instructions, memory and model training, with evaluation and human approval before release.
Read on this pageAn enterprise assistant can receive corrections every day without becoming better at the organisation's work. This paper proposes a governed learning loop for Corporate GPT that turns those interactions into candidate improvements while keeping access boundaries and human oversight in place.
A lesson does not always require retraining a model. It may belong in a business definition, a search improvement, an agent's instructions or a user's memory. The framework routes feedback to the appropriate layer, tests the proposed change against human-verified answers and requires approval before release. For organisations considering continual learning, the paper provides a phased approach that makes evidence, permissions and reversibility part of the improvement process rather than treating every user signal as an automatic update.
User corrections and accepted actions provide signals to investigate, not unquestioned labels.
Improvements can target knowledge, search, instructions or memory before model training.
Candidate changes require evaluation and human approval within the source evidence's permissions.
This is a proposed framework and phased implementation plan, not a report of completed continual-learning gains in Corporate GPT. Benefits cited from external research require validation on the organisation's own tasks, and feedback quality remains a source of uncertainty.
The complete paper, with its original figures, tables and references.
24 pages · Read without leaving this pageDiscuss the approach, evidence and relevance to your team with the people building it.
