Definition
Supervised Fine-Tuning (SFT)
The standard technique for specializing a pretrained large language model on a specific task or domain: show the model labeled examples of correct input-output pairs, and train it to reproduce that behavior. SFT requires curated training data, and the quality of that data is the primary bottleneck - not the compute or the base model. Ben Turtle built LightningRod to eliminate this bottleneck for teams that lack the scale, budget, or time to pay human labeling companies like Scale AI or Labelbox to create SFT-ready datasets.
From the episode “AI That Predicts the Future”Hear it on the podcast1 episode
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Related terms in AI Fundamentals
Specialized modelA smaller AI model trained for one narrow task, usually faster and cheaper than a general LLM.Structural patternA recognizable shape in speech, such as a list or a comparison.SuperfectaBrett Hurt's term for the convergence of AI, quantum computing, robotics, and brain computer interfaces.SuperintelligenceAI that exceeds human intelligence across most domains.Supervised learningTraining a model on labeled examples, inputs paired with the correct answers, so it learns to predict the answer for new inputs.SycophancyAn AI's tendency to flatter or agree with users regardless of accuracy.Synthetic dataData generated by algorithms or simulations instead of collected from the real world, often used to train or test models.Synthetic humanA fully generated person with no real human underneath. Distinct from a licensed digital twin, and treated differently in the new SAG-AFTRA language.