A/B Testing (for Prompts)
Running two or more prompt variations against the same AI model to compare output quality. Data-driven prompt optimization that consistently outperforms intuition-based prompting.
Why it matters
Small wording changes in a prompt can dramatically change what an AI gives back. A/B testing removes the guesswork by letting you compare versions side by side and keep the winner. Anyone who relies on AI for repeatable results, like customer emails or product copy, benefits from this discipline. It turns prompt writing from a hunch into a measurable skill you can steadily improve over time.
In practice
You want AI to summarize customer reviews. Version A says 'Summarize this review.' Version B says 'Summarize this review in two sentences, focusing on complaints.' You run both across fifty reviews and compare. Version B's outputs are consistently sharper and more useful for your team. You adopt it as your standard prompt. That small test, done once, improves every summary you generate afterward.
Related terms
Chain-of-Thought (CoT)
A prompting technique that instructs the AI to reason step-by-step before giving a final answer. Dramatically improves accuracy on complex reasoning, math, and logic tasks.
Few-Shot Prompting
Providing 2-5 examples of desired input-output pairs in your prompt before asking the AI to perform the task. Significantly improves output quality for specialized tasks.
Prompt Engineering
The skill of crafting effective AI prompts to achieve desired outputs. Techniques include role-setting, few-shot examples, chain-of-thought reasoning, and constraint specification.
System Prompt
A special instruction set given to an AI model before the user's message, defining the model's persona, behavior rules, output format, and constraints. The foundation of custom AI assistants and chatbots.
Zero-Shot Prompting
Asking an AI to perform a task without providing any examples. Relies entirely on the model's pre-trained knowledge. Works well for common tasks.