M3-A - What is a Prompt?

May 01, 2026 1:49 PM

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Using above structure make your own instructions file

A prompt is the input or instruction given to an AI model to generate a response. It defines what the AI should do, how it should respond, and the context it should consider.

In simple terms, a prompt is the way a user communicates a task or question to the AI.

To achieve the best results from AI models, it is essential to understand the various ways prompts can be structured to suit different tasks and objectives. There are three primary ways to structure the prompt: direct instructions, open-ended instructions and task-specific instructions.

Direct instructions are clear and specific commands that tell the AI exactly what to do. These prompts are ideal for straightforward tasks where the user has a clear expectation of the output. Direct prompts rely on the model’s ability to parse explicit instructions and generate responses that align closely with the command. The more detailed the instruction, the more likely the output will meet expectations.

Key Components of a Good Prompt

• Instruction: Clearly states what the AI needs to do
• Context: Provides background information or situation
• Input Data: Includes any specific data required for the task
• Constraints: Defines limits such as tone, format, or length
• Output Format: Specifies how the response should be structured

Key techniques in prompt engineering

To maximize the effectiveness of AI models, prompt engineering employs a variety of techniques tailored to different tasks and objectives. The following are several key techniques, each explained with examples of prompts designed to achieve specific outcomes.

To demonstrate the effectiveness of various prompt engineering techniques, Let’s check a single task as the central use case: explaining climate change. The task is framed as follows:

Explain the concept of climate change, its causes, and its effects in a way that is accessible to a general audience.

Each technique approaches the task differently, offering varying levels of guidance, complexity, and methodology. Below, we explore how these techniques can be applied to this use case, with prompts tailored to highlight their unique capabilities.

Zero-shot prompting

Zero-shot prompting involves asking the model to perform a task without providing any prior examples or guidance. It relies entirely on the AI’s pretrained knowledge to interpret and respond to the prompt.

Example prompt:

Explain the concept of climate change, its causes, and its effects in simple terms.

The model is given no prior examples or additional context and must rely solely on its pretrained knowledge to generate the output.

Few-shot prompting

Few-shot prompting includes a small number of examples within the prompt to demonstrate the task to the model. This approach helps the model better understand the context and expected output
Example prompt:

Here are some examples of how to explain complex topics:



- Topic: Photosynthesis

- Explanation: Photosynthesis is the process by which plants convert sunlight, water, and carbon dioxide into energy and oxygen.

- Topic: Gravity

- Explanation: Gravity is the force that pulls objects toward each other, like how the Earth pulls us to its surface.



Now explain: Climate Change.

Show more

By providing a few examples of how to explain other topics, the model is guided on the tone and level of simplicity expected for the climate change explanation.

Chain of thought (CoT) prompting

CoT prompting encourages the model to reason through a problem step by step, breaking it into smaller components to arrive at a logical conclusion

Example prompt:

Step 1: Define what climate change is.

Step 2: Explain the causes of climate change.

Step 3: Describe its effects on the planet.



Now, follow these steps to explain climate change.

The model is encouraged to think step by step, breaking down the explanation into smaller, logical parts for clarity.

Meta prompting

Meta prompting involves asking the model to generate or refine its own prompts to better perform the task. This technique can improve output quality by leveraging the model’s ability to self-direct.

Example prompt:

Create a prompt that will help you explain climate change, its causes, and its effects in simple terms

The model generates its own prompt before attempting to explain the topic, potentially improving the relevance and quality of the output.