Designing a Reliable LLM Workflow for Real-Time Decisions
You are designing an internal LLM assistant for a finance operations team. A user asks: “Can I approve this vendor payment today? If not, what exactly is blocking it and what should I do next?” The correct answer depends on real-time data from two internal systems exposed via APIs: (1) an invoice/PO matching service and (2) a sanctions/AML screening service. The business requires (a) high accuracy, (b) an auditable rationale, and (c) minimal latency/cost.
Write an essay proposing a single end-to-end inference workflow that combines: (i) tool use with external APIs, (ii) a deliberate-then-generate step that surfaces likely error modes before drafting the final response, (iii) a predict-then-verify strategy that generates multiple candidate decisions/explanations, (iv) a verifier that selects or rejects candidates, and (v) a self-reflection step that decides whether to call additional tools or revise the answer.
In your proposal, be explicit about: what the model generates at each stage, when API calls happen, what the verifier checks (and what it cannot guarantee), how self-reflection changes control flow, and the key tradeoffs you are making among accuracy, auditability, and latency/cost. Assume the APIs can occasionally return incomplete data or transient errors.
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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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