Write down which existing behaviors must survive
Build a retention suite from tasks your product already supports. Include routine requests, structured responses, clarification behavior and handling of incomplete information. Choose tasks because customers depend on them, rather than because they are easy to score. Record the evidence needed to judge success and the severity of each failure.
Keep the new-domain suite separate. A combined average can conceal a loss in an established workflow behind a gain on a heavily represented new task. Decide before training which regressions would prevent release and which would require mitigation. The release question is whether the candidate can satisfy both commitments under the actual deployment conditions.
Confirm that the training change caused the regression
Compare the base checkpoint and adapted checkpoint with matching prompts, retrieval inputs, tool definitions and decoding settings. Verify that the intended adapter or model version is loaded. A changed chat template, shortened context or different tool schema can produce a regression that resembles a training problem.
Save the exact inputs and outputs for paired review. If the old checkpoint also fails under the new serving configuration, investigate that configuration before changing the dataset. If the adapted checkpoint fails where the baseline succeeds under matched conditions, you have a more useful training-related hypothesis. Repeat uncertain cases and record variability rather than treating a single different answer as definitive.
Use forgetting as a diagnosis to test
Research on continual fine-tuning observed forgetting across evaluated knowledge, reasoning and reading tasks. Its evaluated setup does not determine what will happen to your checkpoint.
A narrower explanation may be equally useful: the model learned a response convention that conflicts with another workflow, or the training labels overrewarded confident completion. Inspect the failure pattern. Does it lose factual content, mishandle reasoning, use the wrong format or stop asking necessary questions? Those observations suggest different experiments. Calling every regression catastrophic forgetting too early can send the team toward a broad retention remedy when the immediate issue is a particular label or interface assumption.
Look for lessons that conflict with the old product
Illustrative example: a construction assistant is trained on examples that always return a complete specification. The existing product also handles incomplete briefs by requesting missing information. After adaptation, the assistant starts filling gaps instead of asking. A new output habit has undermined an established requirement.
Review the new records for implicit lessons about certainty, scope and response form. Include legitimate incomplete cases and appropriate clarification targets where your application needs them. Check whether the record gives enough evidence for its answer. A professional source file can provide valuable context, but it does not automatically supply the desired behavior or a reviewed answer. Preparation still determines what training teaches.
Change one retention lever at a time
Compare controlled candidates with lower training exposure, a changed learning rate or a different mix of domain and retention examples. If replaying established tasks is feasible, review those examples for relevance and permitted use. Avoid adding an unrelated general corpus merely because it is large.
Keep evaluation suites frozen while comparing candidates. Measure the domain gain and each retention slice rather than ranking candidates by one average. Parameter-efficient adaptation, early stopping or a separate domain endpoint can also be experiments, depending on your architecture. None removes the need to test retained behavior. A separate endpoint adds routing and maintenance obligations, so compare that operating cost with its measured benefits.
Select a checkpoint at the trade-off your product accepts
Evaluate intermediate checkpoints on both suites. The checkpoint with the best domain score may not meet the retention requirement. Record failure severity as well as frequency: an invalid response format and an unsupported business action may carry different product consequences even if each counts as one failed task.
Review disagreements with the same rubric and, where practical, without showing the reviewer which checkpoint produced the answer. Check whether improvements transfer to held-out projects rather than repeated training templates. Keep ordinary use and domain use visible in the report. A candidate is viable when it satisfies the agreed requirements, not when the team can explain away every decline as the cost of specialization.
Preserve a way to detect and reverse the loss
Package the release with the evaluated model configuration, dataset version and regression report. Keep the prior deployment reproducible. Decide which observed production failures trigger further review or rollback, and connect those decisions to the capabilities covered by the retention suite.
When sourcing more domain data, describe the unresolved behavior and the context missing from current records. Ask whether the proposed source material can support that preparation need. More realistic examples may help test the hypothesis, but their origin alone does not guarantee retention. Make the next purchase serve a specific experiment: better task coverage, clearer uncertainty labels or stronger evidence for the new domain skill while preserving established workflows.