How to Make LLM Outputs Reliable for Production Workflows
Transforming probabilistic model outputs into guaranteed deterministic actions.
Making LLM outputs reliable requires shifting from hoping the model follows instructions to enforcing validation at the execution boundary. Exogram validates tool call schemas, state preconditions, and authorization tokens before external APIs or database mutations are allowed to execute.
What Actually Happened in the Real World
A fintech team deployed an automated loan document processor. In 99.4% of cases, it extracted data perfectly. But on handwritten forms with unusual currency symbols, it extracted amounts in yen instead of dollars, nearly wiring $500,000 instead of $3,300 before a human caught it.
Why Polite Prompts Like “Please Don't Do This” Fail
Asking a model to "always double-check currency" fails because models cannot perform real out-of-band sanity checks on their own inferences. They produce fluent numbers that look completely convincing.
The Sub-Millisecond Code Fix
Exogram enforces strict value ranges, rate limits, and external state verification outside the LLM context window. Any action proposing a variance outside allowable bounds is halted instantly.
from exogram import Exogram
exo = Exogram()
@exo.invariant(max_usd_value=10000.0, allowed_currencies=["USD"])
def process_wire(amount: float, currency: str, recipient: str):
return banking_api.transfer(amount, currency, recipient)Frequently Asked Questions
Does this replace Pydantic or JSON schema validation?
Exogram complements JSON schema by verifying not just data types, but business state, financial invariants, and identity permissions.
Related Diagnostic Answers
Comparison Guides
Stop AI Mistakes Before They Execute
Exogram sits directly between your AI model and your tools. Set up in 10 seconds inside Claude, Cursor, ChatGPT, or your own code.