GENERATIVE & LLM · SPEQ SYNTHESIS
GxP GenAI Assistant
A general-purpose generative AI assistant wired into quality and regulatory workflows to draft documents, summarize investigations, reformat content, and answer free-text prompts inside the tools practitioners already use.
Because it generates fluent prose on demand and is not constrained to a grounded corpus, its output looks finished long before it is verified, which is precisely where the assurance burden sits.
What a system class is not
An AI system class describes a shape of system, not a product and not an approval pathway. SPEQ does not qualify, validate, or endorse any implementation, and no regulator recognises these classes as a category.
Weighted score 21/32 (decision consequence and model influence weighted most heavily) places this in the high tier.
Computed deterministically from four Context-of-Use factors — decision consequence, model influence, data sensitivity, and change dynamics. A transparent scoping aid, not a validated risk-assessment system.
SCOPE THE ASSURANCE STRATEGY → CSA WORKBENCHUsed across quality, regulatory, and manufacturing science teams to accelerate drafting and summarizing, influencing the first-draft content that a human author then edits into a controlled record.
Generated text can flow into deviations, CAPAs, protocols, and reports, so an unverified draft that carries a fabricated or misread fact can contaminate a regulated record if a reviewer trusts it.
human in the loop
Every generated output must be reviewed and authored by a qualified person before it enters a controlled record, because the assistant produces unverified drafts that carry no accountability of their own.
- The assistant can hallucinate facts, figures, or citations in a draft, and its fluent style makes fabricated content harder to spot than an obviously incomplete draft would be.
- Summarization can silently omit or distort a material detail from a source investigation, changing the meaning of a record while appearing to faithfully condense it.
- Automation bias and time pressure lead authors to accept generated text with only light editing, weakening the human authorship the workflow depends on for accuracy.
- Users may paste confidential or personal data into prompts, creating a data-integrity and privacy exposure if inputs are retained or used to train external models.
- Model or prompt updates by the vendor can shift tone, behavior, or accuracy without notice, invalidating any prior informal confidence in the assistant’s output.
- Define approved use cases and explicit prohibited uses so the assistant is applied only where a human author reviews the output before it becomes a controlled record.
- Assess and contract the data-handling behavior of the underlying model so prompt inputs containing confidential or personal data are not retained or used for external training.
- Provide reviewer guidance and lightweight verification aids that counter automation bias, keeping the human author genuinely accountable for the accuracy of edited output.
- Track model and prompt versions and re-confirm behavior after any vendor update, since generative output can change materially without a visible signal to users.
- Log prompts and outputs where records are regulated so the provenance of assisted content is reconstructable during an investigation or inspection.
Standards SPEQ maps to this class
AI-governance frameworks
The AI-specific shelf that defines “quality AI” — see Good AI Practice.
SPEQ synthesis — applied AI-assurance judgment to help you scope your own Context-of-Use assessment and validation. Not regulatory guidance, not an AI classification service, and not a substitute for your documented risk assessment.