Q003 — Self-Audit¶


Research	R0020 — Prompt Engineering Gaps
Run	2026-03-25
Query	Q003

ROBIS 4-Domain Audit¶

Domain 1: Eligibility Criteria¶

Rating: Low risk

Criterion	Assessment
Evidence types defined before searching	Yes — vendor documentation and industry guides targeted
Criteria consistent throughout	Yes
Scope maintained	Yes — focused on constraint language in prompt engineering guides

Notes: Stable eligibility criteria throughout.

Domain 2: Search Comprehensiveness¶

Rating: Some concerns

Criterion	Assessment
Multiple search strategies used	Yes — two distinct searches
Searches designed to test each hypothesis	Partial — both searches sought constraint guidance rather than absence
All results dispositioned	Yes — 20 results returned, all dispositioned
Source diversity achieved	Partial — missing OpenAI (403) and Google documentation

Notes: The inability to access OpenAI's documentation is the most significant gap. Two of three major AI vendors are represented, but the third is absent.

Domain 3: Evaluation Consistency¶

Rating: Low risk

Criterion	Assessment
All sources scored using same framework	Yes
Evidence typed consistently	Yes
ACH matrix applied	Yes
Diagnosticity analysis performed	Yes

Notes: Consistent evaluation across sources.

Domain 4: Synthesis Fairness¶

Rating: Low risk

Criterion	Assessment
All hypotheses given fair hearing	Yes
Contradictory evidence surfaced	Yes — Anthropic's "dial back" guidance prominently featured
Confidence calibrated to evidence	Yes
Gaps acknowledged	Yes — missing vendor documentation noted

Notes: The finding that constraints are evolving rather than static adds nuance that neither H1 nor H2 fully captures.

Overall Assessment¶

Overall risk of bias: Low risk

The Anthropic documentation was the most informative and highest-quality source. The missing OpenAI documentation is a limitation but does not invalidate the assessment.

Researcher Bias Check¶

Confirmation bias risk: Low. The research surfaced evidence both for and against imperative constraints, including the unexpected finding that the latest vendor guidance recommends reducing enforcement language.
Recency bias risk: Some concern. Anthropic's "dial back" guidance is model-generation-specific (Claude 4.5/4.6) and may not generalize to all models or vendors.