GENERATIVE & LLM · SPEQ SYNTHESIS
Regulatory RAG Copilot
A retrieval-augmented generation (RAG) assistant that grounds a large language model in a controlled corpus of regulations, guidance, and internal SOPs, then answers practitioner questions with citations back to the retrieved source passages.
Unlike an open chatbot, the copilot is constrained to synthesize only from the documents it retrieves, so its value and its risk both hinge on retrieval quality, corpus currency, and how faithfully the generated answer tracks the cited passage.
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 by quality, regulatory, and validation staff to interpret requirements and locate applicable clauses, influencing how a practitioner scopes a control, drafts a document, or answers an inspector.
Its answers shape regulated interpretations and can propagate into SOPs, assessments, and submissions, so an incorrect or ungrounded response can seed a defective compliance decision downstream.
human in the loop
A qualified practitioner must verify every answer against its cited source before acting, because the copilot influences regulated interpretations yet cannot itself be accountable for a compliance decision.
- The model can hallucinate a requirement, clause number, or effective date that is not present in any retrieved source, presenting fabricated regulatory content with a confident and authoritative tone.
- Retrieval can surface a superseded or out-of-date document version, causing the copilot to answer correctly against an obsolete requirement while appearing current to the reader.
- Answer-to-citation drift lets the generated text assert something the cited passage does not actually support, so a superficial citation check passes while the substance is wrong.
- Automation bias leads practitioners to accept fluent answers without opening the source, gradually eroding the independent verification the grounded design assumes.
- Prompt injection or poisoned documents inside the corpus can steer the model toward misleading answers or leak restricted content beyond the user’s entitlements.
- Establish a controlled, version-managed corpus with defined ingestion and retirement so retrieval can never surface a superseded document as if it were the current authority.
- Evaluate the system on a curated regulatory question set measuring citation faithfulness, retrieval precision, and refusal on out-of-scope queries, not just fluent-sounding answers.
- Require visible, clickable provenance on every answer so the reviewing practitioner can confirm the cited passage genuinely supports the generated claim before relying on it.
- Monitor answer quality and refusal behavior continuously in production, and re-evaluate after any model, prompt, or corpus change that could shift retrieval or generation behavior.
- Enforce entitlement-aware retrieval and prompt-injection defenses so the copilot cannot surface protected content or be steered by adversarial passages inside ingested documents.
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.