Our Training Data
How we work: Research > Reduce to Book Form (a system) > Feed to AI > Get Training Plan > upload our system prompt (Prompt_Protocols) > Pick the next module > Ask the AI to produce the training examples for that module > in our case that’s socratic And
When we build a training plan for one of your books, each module consists of a range of assertions that includes:
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Canonical Assertions –
These are the core, necessary, and sufficient statements of fact, principle, or law in your system. They are crafted for maximum precision and serve as the standard reference points for truth, decidability, and operational rigor. They carry the full weight of the framework and must pass the highest bar for testifiability. -
Adversarial Assertions –
These intentionally introduce edge cases, counterexamples, or potential failure modes. They test whether the system can withstand criticism, falsification attempts, and hostile interpretation. Adversarial assertions ensure the framework isn’t just self-consistent but also resistant to parasitism, ambiguity, or strategic misrepresentation. -
Exploratory or Speculative Assertions (if included) –
These identify open questions, conjectures, or contingent hypotheses that extend beyond current proofs but remain operationally plausible. They guide future research or refinement without diluting the canonical set. -
Didactic Assertions (optional but often useful) –
These restate canonical ideas in simplified, pedagogical, or narrative form for teaching purposes, ensuring accessibility while preserving precision.
So by using PROTOCOLS, training examples in our OUTPUT_CONTRACT in analytic form, and then using the SOCRATIC form we explicitly add the Didactic Assertions by using the socratic form. Sort of ‘belt and suspenders’.
So we use all four set so training assertions to achieve both the accessible interface and the deep interface customers need.
Source date (UTC): 2025-09-10 14:53:08 UTC
Original post: https://x.com/i/articles/1965790649195872617
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