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How utility analysts use AI to prep witnesses for cross-examination at the PUC.
AI can generate likely cross-examination questions from filed testimony to harden witness prep.
A utility rate case is a regulatory proceeding before a Public Utility Commission (PUC) in which the utility presents its case for a rate change. Utility witnesses — typically engineers, economists, and policy analysts — submit pre-filed testimony covering their portion of the rate case: cost-of-service studies, capital expenditure justifications, demand forecasts, depreciation methodologies. Intervenors (consumer advocates, industrial customers, environmental groups) file their own testimony and then cross-examine the utility witnesses in technical hearings. Cross-examination at a PUC proceeding is methodical and targeted: intervenors study the pre-filed testimony carefully and prepare questions designed to expose inconsistencies, challenge assumptions, or establish records they can reference in briefs. AI is a strong tool for witness prep in this environment because it can read the utility's pre-filed testimony and the intervenors' briefs and generate likely cross-examination question sets organized by topic. A utility analyst can use AI to produce a preliminary question list, then work with witnesses to develop answers. What AI cannot do is predict how commissioners will rule on contested issues, replace counsel's strategic judgment about which concessions to make, or coach a witness to be truthful under oath — that constraint runs the other direction.
15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-careers-ai-utility-rate-case-witness-prep-r10a4-adults
What is a utility rate case?
Who are 'intervenors' in a utility rate case?
What is 'pre-filed testimony' in a rate case proceeding?
How does AI improve the quality of witness prep question sets in rate cases?
A utility analyst uses AI to prepare cross-examination questions from an intervenor brief. What critical review must happen before using these with a witness?
Why must witnesses answer truthfully, even if AI helped prepare their answers?
What does 'clustering questions by topic' mean in the context of AI-assisted witness prep?
What is a 'test year' in a rate case?
What can AI NOT do in rate case witness preparation?
What is the 'adversarial question prompt' approach described for witness prep?
Why is early witness preparation more valuable than last-minute preparation?
What is the difference between 'tracking inconsistencies across exhibits' and 'making design decisions' in rate case context?
A utility witness is asked a question at the PUC hearing that AI preparation did not anticipate. What should they do?
What is a 'cost-of-service study' and why does it matter for cross-examination prep?
What is the overall benefit of AI-assisted witness preparation for utility rate cases?