How to use LLM Finance Error Taxonomy
12 documented LLM-on-finance failure modes (hallucinated ticker, stale price, units, currency, off-by-100, fictional source, more). Paste an LLM output and the page flags which categories trigger so you can triage fast.
What It Does
Use the calculator with intent
12 documented LLM-on-finance failure modes (hallucinated ticker, stale price, units, currency, off-by-100, fictional source, more). Paste an LLM output and the page flags which categories trigger so you can triage fast.
Engineers debugging LLM-driven finance pipelines who need a structured taxonomy of failure modes rather than chasing each bug fresh every time.
Interpreting Results
Each triggered category is a flag, not a verdict. Most triggers are false alarms but each one should be reviewed by a human before the output goes downstream. Off-by-100 is the most insidious — the answer looks right at a glance.
Input Steps
Field by field
- 1
Browse
Browse the six top-level categories: factual, reasoning, arithmetic, formatting, refusal, prompt-injection.
- 2
Drill
Drill into a category to see specific failure modes with example prompts and expected vs. observed outputs.
- 3
Use result
Use the category structure to design your own evals: pick the categories most relevant to your task.
- 4
Reference
Reference the per-model error rates on the methodology page when choosing a model — error profile matters more than aggregate accuracy.
- 5
Step 5
Re-check the taxonomy after each major model release; error rates shift with new versions.
Common Scenarios
Use realistic starting points
Quarterly earnings extraction
Output type
table of financial numbers
Off-by-100 (basis points vs percent) and currency confusion are the most common triggers; rarely actual hallucination if the source was provided.
Macro analysis output
Output type
narrative analysis with cited stats
Fictional source and stale price more common here; LLM may cite a Bloomberg link that doesn't exist or quote a price from 6 months ago.
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Paste a source document + an LLM's extraction. Every numeric claim in the output is checked against the source. Client-side. Catches silent fabrication.
Structured Schema Validator for Finance
Paste LLM JSON output and validate against four pre-built finance schemas — research output, trade decision, risk snapshot, peer comparison — with sanity.
Prompt Regression Tester
Run the same prompt against multiple models (Claude 4.5/4.6/4.7, GPT-5, Gemini 2.5) with your own keys. Diff outputs, score drift, catch regressions.
FAQ
Questions people ask next
The short answers readers usually want after the first pass.
Related Content
Keep the topic connected
Hallucination Detection
Detecting LLM hallucinations in financial outputs: the verifiable-claim approach, citation grounding, and cross-model agreement signals that work.
Model Drift
Model drift: when an LLM's behavior changes between calls, versions, or weeks. The monitoring stack that catches it before production breaks.
Prompt Injection
Prompt injection: when untrusted text in a prompt overrides system instructions. The attack patterns and the structural defenses that work in production.