Definition
Grounded Training Data
Training data derived directly from real-world, timestamped sources - internal Slack channels, company reports, PDFs, emails, public news archives - rather than generated synthetically by AI. Ben Turtle of LightningRod AI defines grounded data as the antidote to synthetic slop: it contains information the model doesn't already have, sourced from events that actually happened in a specific domain. Grounded training data is what enables a small model to outperform a frontier model on domain-specific tasks - not because the small model is smarter, but because it was taught things the frontier model was never shown.
From the episode “AI That Predicts the Future”Hear it on the podcast1 episode
Learn the language of AI-native founders
Every week: the new terms, tools, and workflows founders are using, straight from the podcast.
Related terms in AI Fundamentals
GAN (Generative Adversarial Network)A setup where two neural networks compete: a generator creates fake data and a discriminator tries to spot it, which pushes the generator to get more realistic.Generative AIAI that creates new content, such as text, images, music, code, or video, rather than only analyzing existing data.GPT (Generative Pre-trained Transformer)A family of large language models built on the transformer architecture and trained to generate human-like text from a prompt.GPT WrapperA thin AI product built on top of a foundational LLM without proprietary data, unique distribution, or genuine differentiation. The category of AI product most founders are building and fewest investors want to fund as foundational models grow more capable. Lacks the defensible IP or distribution moat required for long-term pricing power.GroundingStanding barefoot on soil or grass. The claimed mechanism involves electron transfer from the earth; the claim isn't established science, and Steve Phillips says so himself.GuardrailsRules built into an AI product to detect risk and route users to proper help.HallucinationWhen an AI model produces information that is false or made up but presents it as fact.Hallucination ToleranceThe acceptable error rate for AI outputs in a given system context. In consumer chatbots, high hallucination tolerance is acceptable — wrong answers are annoying but not dangerous. In healthcare eligibility determinations, medication routing, or financial compliance, the tolerance is zero — errors create legal liability, financial harm, or patient safety risks. Designing for zero hallucination tolerance requires architectural decisions (human-in-the-loop checkpoints, output traceability, auditability standards) rather than simply selecting better models. Softheon's target: 99.9999999% accuracy — exceeding air traffic control standards.