Background
A software company wanted to use a generative model to produce technical documentation drafts from structured engineering data, but standard fine-tuning of an off-the-shelf language model produced outputs that were fluent but frequently factually inconsistent with the source data.
The Challenge
Existing fine-tuning and prompt-engineering techniques could not reliably constrain the model’s output to remain factually grounded in structured source data across varied document types.
Technological Uncertainty
It was not known whether any combination of retrieval grounding, output constraints, and fine-tuning could achieve the required factual consistency without degrading output fluency, given the variability of the underlying structured data.
Experimental Development
The team tested multiple retrieval-augmented generation configurations, structured-output constraint methods, and post-generation verification techniques, measuring factual consistency rates against a manually annotated test set after each change.
What Failed?
A pure retrieval-augmentation approach reduced fabrication but introduced awkward, disjointed phrasing, and an initial verification-and-regeneration loop was too slow for practical use, requiring a redesign of the constraint mechanism.
Technological Advancement
The team developed a constrained-generation approach combining structured retrieval with inline consistency checks during generation, producing new technical understanding of how to balance factual grounding against output quality for this data structure.
Potentially Relevant SR&ED Activities
- Systematic testing of retrieval-augmented generation configurations
- Development and testing of output-constraint and verification mechanisms
- Iterative measurement of factual consistency versus fluency trade-offs
What Would Generally Not Qualify
Standard prompt-engineering adjustments to an unmodified third-party model, without addressing an underlying technical constraint, would generally not qualify on their own.
Documentation
Annotated test results tracking factual consistency across iterations, technical notes on failed constraint approaches, and architecture decision records would support this claim.
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