Definition
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.
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Related terms in AI Fundamentals
Foundation modelA large model trained on broad data that can be adapted to many different tasks, serving as the base for many products.Foundation Model Fine-TuningThe process of taking a pre-trained large language model (such as GPT-4, Claude, or Gemini) and continuing its training on a smaller, domain-specific dataset to specialize its behavior for a particular use case without training a model from scratch. Fine-tuning allows companies to capture the reasoning and language capabilities of a foundation model while customizing it for specific vocabularies, response formats, constraints, or domain knowledge. Loman.ai fine-tunes foundation models for the specific context of restaurant phone conversations - teaching them the nuances of taking food orders, handling 86'd items, managing reservation requests, and maintaining the appropriate tone for different restaurant types. Christian's approach: test every major foundation model, identify which performs best for each specific sub-task within the product, and fine-tune for production performance.Frontier labsThe handful of companies training the most capable models, including Anthropic, OpenAI, and Google.Frontier modelOne of the most capable general-purpose AI models available, at or near the leading edge of the field.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.Grounded Training DataTraining 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.