Source Grounding

Core principle

Every AI-assisted output must be traceable to specific source materials. No unsourced claims. No invented data. No extrapolation beyond what the evidence supports.

Translation, not invention.

What source grounding means

In medical writing, the source material is the foundation. Whether it is a clinical study report, a published paper, a summary of product characteristics, or a set of congress abstracts, the source defines what can and cannot be said. AI does not know what the source says unless you give it the source. And even when you do, AI can:

Paraphrase in ways that subtly shift meaning
Fill gaps with plausible-sounding but unsupported statements
Merge findings from different sources without distinguishing them
Present interpretations as facts

Source grounding is the practice of ensuring that every AI-assisted output remains anchored to the materials it was given, and that a human can trace every claim back to its origin.

How to implement source grounding

1. Always provide source materials as input

Never ask an AI to write about a topic from general knowledge. Always provide:

The specific paper, CSR section, or data source
The relevant prescribing information or SmPC
The approved key messages or brand messaging framework

If you do not have a source, you do not have an input for an AI workflow.

2. Instruct the AI to cite its sources

When using prompt patterns from this playbook, you will see instructions like:

“Base your output only on the provided source. Do not include information from outside the source material. Cite specific sections, tables, or figures where relevant.”

This is not a suggestion — it is a structural requirement. AI models will generate plausible content from training data if not explicitly constrained to the provided source.

3. Verify every claim against the original

After receiving AI output:

Check each factual claim against the source document
Confirm that numerical data (endpoints, p-values, confidence intervals, sample sizes) are accurately reproduced
Verify that the AI has not combined findings from different study arms, timepoints, or populations
Confirm that conclusions match the source’s stated conclusions, not the AI’s interpretation

4. Flag and remove unsourced content

If the AI has included any statement that cannot be traced to the provided source:

Remove it, or
Source it from an appropriate reference, or
Flag it for expert review

Do not leave unsourced claims in a deliverable on the assumption that they are “probably correct.”

Common source grounding failures

Failure	Example	Impact
Hallucinated data	AI inserts a p-value of 0.003 when the source reports p=0.03, or generates a confidence interval that does not appear anywhere in the paper	Fabricated statistical evidence enters a slide deck or manuscript draft. If not caught in review, it propagates through all downstream deliverables.
Merged populations	AI combines ITT results (n=450) with per-protocol results (n=380) into a single statement, blurring the analysis population	Misleading efficacy representation. An MLR reviewer or journal editor will catch this — but only after wasted review cycles.
Extrapolated conclusions	Source reports non-inferiority (HR 0.95; 95% CI 0.82–1.10). AI summary states the treatment “demonstrated improved outcomes”	Overstated claim that could become a compliance issue if it enters promotional materials.
Invented context	AI adds a sentence about disease prevalence or mechanism of action from its training data, not from the provided source	Unsourced claims enter the deliverable. The writer may not notice because the information sounds plausible and relevant.
Omitted qualifiers	Source states efficacy “in patients with moderate-to-severe disease (PASI ≥12 at baseline).” AI summary drops the qualifier.	Claim appears to apply to the full study population. In a promotional context, this broadens the claim beyond what the reference supports.