Inception report QA, and the check that passes everything.
An inception report is the document a client reads to decide whether the evaluation is going to hold up. It sets out the theory of change interrogation, the evaluation matrix, the methods, the sampling, the workplan, and the risks. Errors at this stage propagate through every subsequent stage. A misspecified sub-question produces a misdirected data collection instrument. An unmapped assumption produces an analysis that misses what it should have tested. Inception QA is the mechanical check that catches those errors before they compound.
The failures that hide inside a clean-looking check.
The most consequential failure when AI is used for inception report QA is false negatives. The model reads the draft, cross-references against the evidence base, and reports that everything is consistent when it isn't. A factual error slips through because it appeared in a source the model gave less weight to. A logical inconsistency between two sections goes undetected because the sections were far apart in the document and the model's attention drifted between them. An outdated statistic gets passed because the model didn't recognise that a more recent version existed in the evidence base. These are the failures that undermine the entire purpose of the workflow.
The second failure is false positives at scale. The model flags twenty items per report where only three warrant evaluator attention. When the false positive rate is high, evaluator review of AI flags becomes a performance rather than a substantive check. Real errors get missed inside the noise. This is more damaging than it appears because it is hard to detect from the outside. A workflow producing many low-value flags looks thorough. It is often less thorough than a workflow producing fewer, better-calibrated flags.
Four structural controls, each answering a specific failure mode.
Structured checking against defined categories answers false negatives. The QA workflow does not ask the AI to identify errors in general. It asks the AI to check the draft against defined categories of finding, one category at a time. Factual claims against source. Internal consistency between sections. Alignment between findings and recommendations. Coverage of the evaluation matrix. Currency of data cited. Compliance with the ToR. Each category is a separate pass through the document with its own prompts and its own outputs. This structured approach catches more errors than open-ended QA prompts because it forces the model to look for specific problems rather than to look for problems in general.
Calibrated flagging answers false positives at scale. Every flag the AI raises is required to include a confidence level, a specific citation of the source the AI is referencing, and a suggested action. Flags below a defined confidence threshold are surfaced separately for evaluator awareness rather than mixed into the main review queue. This means the evaluator's substantive review is focused on the flags most likely to warrant attention, while lower-confidence flags remain visible without dominating the workflow. The threshold is tuned per assignment and documented in the disclosure.
A structured flag register, category summaries, evaluator sign-offs, a disclosure.
Every Meridian inception QA assignment produces four artefacts. The structured flag register, listing every flag the AI raised, with category, confidence level, source citation, and suggested action, together with the evaluator's disposition on each. The category summaries, showing what was checked in each of the QA categories and what the pass-or-flag distribution looked like. The evaluator sign-offs, category by category, with any residual concerns documented rather than dismissed. And the disclosure text, a short methodology annex drafted for inclusion in the inception report.
The four artefacts together mean the QA workflow is inspectable. When a client asks what was checked and what was found, the answer is a structured register. When a client asks about the AI's role, the answer is a category-by-category breakdown of what the AI proposed and what the evaluator confirmed. When a client asks about limitations, the answer is the residual concerns section of the sign-offs.
The differentiation is the discipline.
Inception QA is a check, not an analysis. The failure mode that matters most is thoroughness that performs itself: many low-value flags that look rigorous and hide the ones that count. Calibrated flagging and structured category-by-category checking exist because a report producing twenty flags is often catching fewer real errors than one producing three.