// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Instruction Tuning
A training method where an AI model is taught to follow instructions by being shown many examples of tasks described as instructions and their correct answers.
TECHNICAL DEFINITION
Instruction tuning is a fine-tuning technique for large language models (LLMs) where the model is trained on a dataset of diverse tasks formatted as natural language instructions and their corresponding outputs, enhancing its ability to generalize to new, unseen instructions.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt contexts supplied to the GenAI model, such as metadata, API tools, and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Instruction following
- Task-oriented fine-tuning
- Prompt-based fine-tuning
USAGE NOTE
Instruction tuning significantly improves an LLM's ability to understand and execute user commands.