AI Glossary · Prompt Engineering
Instruction Tuning
Post-training on instruction–response pairs so the base model follows natural-language instructions reliably.
Definition
What is Instruction Tuning?
Instruction Tuning is post-training on instruction–response pairs so the base model follows natural-language instructions reliably.
- Category
- Prompt Engineering
- Glossary set
- 11 related terms
- Audience
- Enterprise AI leaders
Why does Instruction Tuning matter in enterprise AI?
Instruction Tuning matters in enterprise AI programs because it helps business and technology leaders align vocabulary, scope, ownership, and measurable outcomes.
Related terms in Prompt Engineering
- Few-Shot Prompting
- Including a small number of input–output examples in the prompt to demonstrate the desired format and reasoning.
- Meta-Prompting
- Using an LLM to draft, critique or optimize prompts for another LLM — a scalable alternative to hand-crafting.
- Persona Prompting
- Assigning a role ("You are a senior claims adjuster…") to steer tone, depth and vocabulary of the output.
- Prompt Chaining
- Decomposing a task into a sequence of prompts where each step's output feeds the next; the poor-man's agent.
- Prompt Engineering
- The practice of designing inputs (instructions, examples, structure) that reliably elicit the desired model behavior.
- Prompt Template
- A parameterized prompt with variables for dynamic values — the atomic unit of prompt versioning and A/B testing.