DEFINING “CORRECT” IN AI SYSTEMS

No Clear Definition of Correct

As IT, software, and consulting teams adopt LLMs across products and workflows, AI is often deployed without a clear testing process or definition of correctness.

Common Symptoms:

• “We donʼt know if the output is correct
• Changes are shipped without acceptance criteria
• AI decisions become overly rigid (blocking valid cases), or overly permissive (allowing incorrect outputs)

Teams lose confidence in AI decisions and ship changes without knowing if they are right or wrong.

What This Means for the Business

• Unpredictable output quality
• Increased back-and-forth between teams and stakeholders
• Higher risk of shipping flawed logic to production
• Difficulty explaining or defending AI decisions to clients
• Slower iteration due to a lack of shared evaluation standards

From Subjective Judgment to Testable Expectations

TrustForge.AI helps teams move from guessing whether AI outputs are correct to checking them against clearly defined rules.

How TrustForge.AI Solves It:

Step 1 - Define "Correct"

Teams clearly define what good and bad AI output looks like.

Step 2 - Set a Reference Standard

Golden datasets establish expected outputs as a single source of truth.

Step 3 - Test Before Shipping

Every AI change is automatically tested, and failures are flagged before deployment.

What Changes After Applying TrustForge.AI

• A clear definition of correct AI output
• Clear rules for approving AI changes
• Consistent AI decisions across teams
• Less debate and rework in reviews
• More confidence when deploying AI
• Easier to explain AI results to clients

AI systems cannot be trusted if “correct” is undefined. 

Through its Trust-Forge.ai framework for AI testing services, Tesvan transforms vague expectations into clear, testable standards, enabling IT, software, and consulting teams to build, validate, and scale AI systems with confidence