Category:
Category:
Instruction Tuning
Category:
Core AI & LLM Concepts
Definition
Training LLMs on curated instruction–response datasets.
Explanation
Instruction tuning improves an LLM’s ability to follow directions by exposing it to large sets of tasks with ideal responses. It increases consistency, alignment, reasoning reliability, and generalization. Many leading models (GPT, Claude, Llama) rely on instruction tuning as a core step of training.
Technical Architecture
Instruction Dataset → SFT Training Loop → Tuned Model → Evaluation → Deployment
Core Component
Labeled instruction dataset, trainer, evaluation pipeline, model weights
Use Cases
Enterprise copilots, domain assistants, routing models, alignment processes
Pitfalls
Poor dataset quality leads to model degradation; overfitting; catastrophic forgetting
LLM Keywords
Instruction Tuning, SFT, Supervised Fine Tuning
Related Concepts
Related Frameworks
• Fine-Tuning
• Prompt Engineering
• Alignment
• SFT Workflow Diagram