Prompt Engineering has emerged as a crucial technique to maximize the effectiveness of AI models.
Prompt Engineering has become popular with the rise of Large Language Models such as ChatGPT, despite being around for decades.
What's a Prompt?
Prompt is the input that guides the AI model to generate a response.
Prompts are the initial query that sets the context for the AI system. In other words, it is the starting point of the interaction between humans and machines. A well-defined
Prompt guides the AI model behavior in the desired direction.
What Are Prompt Types?
Prompts fall into five categories:
Conditional Prompts, and
1. Instructional Prompts
Instructional Prompts provide explicit instructions to the AI model about the expected response, such as "Write a summary of the given text." which tells the AI to generate a concise summary.
2. Completion Prompts
Completion Prompts involve providing information and asking the AI model to continue or complete it. For example,
Completion Prompts can initiate text generation, where the model continues a paragraph or story.
3. Question-Answer Prompts
Q&A Prompts, or
Question-Answering Prompts involve asking questions to receive accurate answers. Chatbots, voicebots, and information retrieval systems are examples of
4. Conditional Prompts
Conditional Prompts add a specific condition or context to the input, such as “If the weather is rainy, suggest indoor activities."
5. Action Prompts
Action Prompts or
Actionable Prompts instruct the AI model to perform a particular task or operation in response to the input. “Set a reminder for tomorrow at 3 PM to call John.” and “Transfer my call to an agent” are examples of
What's Prompt Engineering?
Prompt Engineering is the process of crafting well-defined
Prompts to achieve desired outcomes from AI models. It may require understanding the capability and limitations of models to tailor the
Prompts. We have some tips to excel in
Promptsclear, concise, and unambiguous.
- Experiment with variations to find the one that yields the best results when needed.
- Fine-tune the AI models to adapt desired output and behavior.
- Understand the training data.
- Incorporate context into your
Promptsto guide the model responses.
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