Post-Incident Analysis: Preventing Confidently Wrong API-Backed Answers
You lead an LLM platform team. A customer-facing assistant can call two internal APIs: (1) get_account_balance(account_id) and (2) get_recent_transactions(account_id, days). Last week, the assistant told a customer they had “$0 available” and recommended a payment plan. An audit later showed the model (a) called the correct APIs but (b) misread a negative pending authorization as the final balance and (c) produced a confident explanation that sounded plausible. You are asked to propose a revised inference-time workflow that reduces the chance of this kind of error without adding more than ~1 second median latency.
Write an essay that (i) designs a concrete end-to-end flow combining deliberate-then-generate, self-reflection, and a predict-then-verify stage; (ii) specifies what the verifier checks and whether it should be outcome-based, process-based, or a hybrid; and (iii) explains how and when the model should use the external APIs (including what to do when API outputs are ambiguous or inconsistent). Your answer must explicitly discuss the tradeoffs among accuracy, latency, and failure modes (e.g., false rejects vs false accepts), and justify why your design would have prevented the incident described.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.5 Inference - Foundations of Large Language Models
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