How do I ensure my feature summary workflow with a spec sheet parser is compliant with fact-checking requirements?

To keep a feature-summary workflow compliant with fact-checking requirements, treat the spec sheet parser as a retrieval and structuring tool, not an authority. Your workflow should be designed so every summary claim can be traced back to source text and independently verified.

Practical compliance checklist

1) Use only authoritative source documents

• Prefer manufacturer spec sheets, official datasheets, regulatory filings, or primary documentation.
• Avoid relying on marketing pages, reseller listings, or generated content as the only source.

2) Preserve source provenance

For every extracted feature, store:

• document title
• version/date
• page/section number
• exact quoted text or extracted table cell
• parser confidence, if available

This makes every summary line auditable.

3) Separate extraction from interpretation

Have the parser do one job:

• extract fields, units, and values

Then have a second step:

• generate a human-readable summary

Do not let the summarizer invent missing values or infer features that are not explicitly stated.

4) Require evidence-backed claims

Each summary sentence should map to one or more source spans. For example:

• “Supports Wi‑Fi 6” only if the spec explicitly says “Wi‑Fi 6” or “802.11ax.”
• “Battery life is 10 hours” only if the spec states that under a named test condition.

If the evidence is ambiguous, label it as such:

• “The sheet indicates X, but the test conditions are not specified.”

5) Normalize carefully

When converting units or reconciling values:

• keep the original value
• show the conversion logic
• do not round in a way that changes meaning

Example:

• source: “9.8 in”
• summary: “9.8 in (24.9 cm)”

6) Handle missing or conflicting data explicitly

If two sources disagree:

• do not silently choose one
• surface the conflict
• note which source is newer or more authoritative, if known

Example:

• “Source A lists 12 GB RAM; Source B lists 16 GB RAM. The manufacturer’s latest datasheet should be used to resolve the discrepancy.”

7) Add a human review gate for high-impact claims

For regulated, safety-related, financial, medical, or legal claims:

• require manual review before publishing
• use the parser output as draft evidence, not final truth

8) Log the full workflow

Keep an audit trail of:

• input document hashes
• parser version
• extraction rules/prompts
• transformation steps
• final summary text
• reviewer approvals or edits

This is crucial for compliance and reproducibility.

9) Avoid unsupported inference

Do not infer:

• performance rankings
• compatibility not explicitly stated
• compliance status unless the document explicitly certifies it
• “best,” “fastest,” “industry-leading,” etc., unless backed by evidence

10) Add validation rules

Use automated checks such as:

• numeric fields must match source values
• units must be recognized
• every claim must have at least one evidence link
• summaries with low-confidence extraction cannot publish automatically

Recommended workflow

1. Ingest authoritative spec sheet.
2. Parse and extract structured fields.
3. Attach evidence spans to each field.
4. Run rule-based validation for completeness and consistency.
5. Generate summary only from validated fields.
6. Flag uncertainty, conflicts, or missing data.
7. Send high-risk summaries to human reviewer.
8. Store audit logs and source references.

A simple policy statement

You can formalize it as:

“All feature summaries must be directly supported by source documentation. The parser may extract and normalize information, but it may not infer or extrapolate beyond the text. Every published claim must include traceable evidence and, where applicable, human review.”

If you want, I can help you turn this into:

• a compliance checklist
• a workflow diagram
• or a JSON schema for evidence-backed feature summaries.