Failure Modes, Verification & the Primary-Source Guardrail

Learning objectives

By the end you can:

1. (Understanding (gated) State from memory the root cause of AI error) the model optimizes for plausible, not true, and explain, in one breath, why fluency is not evidence of accuracy and why a confident tone carries no information about reliability.
2. (Understanding (gated) Name and define the seven failure modes) hallucinated numbers, fabricated citations, sycophancy, data-cutoff staleness, unit/scale/period errors, prompt injection, and overconfidence, and give a concrete example of each on a real analyst task.
3. (Understanding, gated) Recite the Primary-Source Guardrail (checks 1–5) (Trace, Match, Recompute, Date-check, Cite) and map every failure mode to the specific check(s) that catch it, so the response to any suspicious output is a routine, not a guess.
4. (Understanding, gated) Detect and correct, against a bundled source, an India scale trap (₹ crore vs ₹ mn vs ₹ lakh) and a US period trap (fiscal year vs calendar year; standalone vs consolidated), computing the corrected figure yourself.
5. (Understanding (gated) Run the detection reflex) list every figure and claim in an AI output, then Trace → Match → Recompute → Date-check → Cite each one, as a standing routine fast enough to use on every deliverable.
6. (Understanding, gated) Recognize prompt injection as untrusted instructions hiding inside data, and neutralize it by treating all tool/document/agent output as data to be verified, never as commands to be obeyed.
7. (Understanding, gated) Resist sycophancy: hold a figure to the source and not to the model's agreement, and never let your own pushback (or the model's apology) move a number that the source did not move.
8. (Productivity (tool awareness) Use AI deliberately to speed verification) triage which claims to check first, normalize units and periods, and red-team your own draft, while keeping the final trace-and-recompute in your own hands, and know exactly where each tool's help stops.
9. (Understanding, gated) Produce a clean verification log for a mixed India+US AI output: every figure marked Supported / Corrected / Rejected, each with its source location and the Guardrail check that resolved it, the artifact that lets a reviewer re-verify your work.

Prerequisites & connections

Builds on. This node is the payoff of the whole AI branch. It assumes AI0.01 (why a model produces fluent-but-wrong output, the mechanism, not just the slogan), AI0.02 (the tool categories, so you know where a given output came from and where its data went), AI0.03 (asking for citations, confidence, and uncertainty, the prompting habits that make verification easier), AI0.04 (RAG and its honest boundary, grounding reduces but never eliminates hallucination, because the model can still misread a retrieved chunk), and AI0.05 (agents, and the way multi-step tool use lets a single early error quietly compound). It also leans on the analyst core: you cannot Trace a figure you cannot read, so the statement-reading fluency of Phase 1 (M1.02–M1.05: spreading a P&L, standalone vs consolidated, the shape of an Indian annual report and a US 10-K) and the by-hand arithmetic and unit discipline of the Excel track (Recompute is nothing but re-adding the column yourself) are load-bearing here. The reusable Guardrail artifact: guides/ai-primary-source-guardrail.md, is the standing reference this module drills.

Feeds forward. Everything AI-augmented downstream stands on this. AI0.07 (applying AI across research, modeling, memo drafting, decks, coding, and data analysis, plus ethics, disclosure, and privacy) is just this Guardrail applied verified-pass by verified-pass across an analyst's whole workflow, the capstone there is graded by a verification worksheet whose logic is exactly what you build here. Every ai-augment-json callout in every galaxy of the program (Finance, Excel, Consulting, Presentation) ends in a verify field, and that field is always a task-specific instance of these same five checks; this module is where you learn to run them. And it is the operational form of the program's non-negotiable rule #7 (from the Master Map): primary sources outrank every summary, including AI output, any number in a memo must trace to a filing, an exchange, a regulator, or a primary dataset. When a colleague three years from now hands you a slick AI-written company summary and asks "can we use this?", the reflex you drill in the next fourteen hours is the entire difference between "yes, I checked every number" and an expensive apology.