Step through a real test case and see how the analysis fits together.
A test argument from my evaluation suite
Case eval_003 tests whether the analysis catches unsupported marketing claims.
You should buy this supplement because celebrities use it. It has no side effects. Big pharma doesn't want you to know about it. Thousands of five-star reviews prove it works.
Turn the argument into claims
The ingestion agent extracts a claim tree. It distinguishes premises, conclusions, and supporting claims before analysis begins.
In this example, the recommendation to buy the supplement depends on celebrity use, reviews, and unsupported safety claims.
Three checks, working in parallel
Logic mapper
Maps how the claims support or contradict one another.
Fallacy detector
Looks for named reasoning errors in the argument.
Fact checker
Checks verifiable claims against external sources.
The analysis graph waits for these branches before scoring the argument.
Combine findings, then check the test targets
The scorer receives the claim tree, argument graph, detected fallacies, and fact checks.
This evaluation case expects an overall score between 0 and 35 and checks for these fallacies:
Appeal to Authority
Conspiracy Theory Fallacy
Hasty Generalization
Appeal to Ignorance
Ad Hominem
Bandwagon
Appeal to Popularity
These are the evaluation targets defined in the repository. The walkthrough is a guided example, not a live model run.
Step 1 of 4
03 / MY APPROACH
How I work.
I work on model training, evaluation, and the software that uses the model.
My projects include training loops, retrieval, tool calls, and deployment. I use tests and logs to check outputs, track failures, and understand response times.