The 6 Components of a Good Prompt

Understand the essential components of a good prompt and how to craft effective instructions for AI models.

Components of a Good Prompt

Published: Monday, Nov 10 2025

The Anatomy of an AI Request: Six Components of a Powerful Prompt

In the world of Generative AI, the models are brilliant, but they are not mind-readers. The difference between a vague, generic answer and a precise, actionable result often comes down to one thing: the quality of your prompt.

A prompt is more than just a question; it's an instruction set. Building an effective instruction set requires understanding its core components. Whether you are generating code, summarizing a document, or creating an AI agent for a trading platform like Neptune, you need structure.

We've broken down the anatomy of a perfect prompt into six essential components—the same structure we use when training our AI decision layers. Mastering these six elements will transform your interactions with any Large Language Model (LLM).

I. The Six Pillars of Prompt Engineering

A good prompt should move beyond a simple command and provide the LLM with everything it needs to know about who it is, what it's doing, and how the output should look.

1. Persona: Set the Identity

The Persona sets the tone, voice, and expertise of the model's response. Without a persona, the model speaks as a generic helper. With one, it speaks with authority and context.

  • Goal: Define who the model is and its mindset.
  • Example (Vague): "Explain how options trading works."
  • Example (Good): "You are a quantitative finance specialist with 15 years of experience on the NASDAQ floor. Explain how options trading works for a new retail investor."

2. Task / Steps: Define the Action

The Task / Steps component is the core instruction. It must be detailed, linear, and leave no room for ambiguity. Break down complex tasks into a simple, numbered list for the model to follow sequentially.

  • Goal: Describe the desired action as a step-by-step process.
  • Example (Vague): "Write me a summary of my emails."
  • Example (Good): "Scan the 10 most recent emails. For each email, identify the sender and the key action item. Finally, compile these into a single summary table listing Sender, Subject, and Required Action."

3. Context: Provide the Constraints

The Context helps the model understand the boundaries, constraints, and specific details of your request. This often includes timeframes, source material, or necessary background information.

  • Goal: Help the model understand the constraints and details of what you're asking. The more specific, the better.
  • Example (Vague): "Analyze this code snippet."
  • Example (Good): "Analyze the following PHP code block. The constraint is that the output must assume a Laravel v10 environment and focus only on potential security vulnerabilities related to SQL injection. Ignore style and performance concerns."

II. Shaping the Output and Refining the Result

4. Format: Stipulate the Structure

The Format is non-negotiable for developers and content creators. It ensures the output is immediately usable, whether it’s a JSON object for an API or a markdown table for a blog post.

  • Goal: Ask for the response to be formatted precisely (table, bulleted list, JSON, code block, etc.).
  • Example (Vague): "List the top Python packages for data science."
  • Example (Good): "List the top five Python packages for data science, ensuring the response is formatted as an HTML table with three columns: Package Name, Primary Use, and GitHub Stars (using a placeholder value)."

5. Examples: Show the Way (Few-Shot Learning)

Providing Examples—often called "few-shot learning"—is the single most powerful way to control style, tone, and accuracy. By showing the model exactly what you want to see, you drastically improve the fidelity of the output.

  • Goal: Give an example of the desired output style, complexity, or structure.
  • Example (Vague): "Write a technical summary of the new GPU."
  • Example (Good): "Write a technical summary of the new GPU. Here is an example of the required tone and technical depth (copy-paste a prior, perfect summary for a different product)."

6. Followup: Initiate the Chain of Prompts

The Followup is the recognition that AI is a conversation, not a single query. Don’t overlook the ability to refine the original ask with additional questions or probes—this is often called the "chain of prompts."

  • Goal: Don't stop at the first answer; refine, iterate, and dive deeper.
  • Example: "The initial article you wrote is great, but now, re-write Section III to be less formal and include a relevant real-world analogy."

III. Conclusion: Prompts as Engineering Specifications

If you treat your prompts like engineering specifications—detailed, constrained, and unambiguous—you will get predictable, high-quality results. Poor prompt quality is the leading cause of "AI hallucination" and irrelevant output.

For your next task, run through the six-component checklist:

  1. Persona: Who is talking?
  2. Task: What are the steps?
  3. Context: What are the rules?
  4. Format: What should the final product look like?
  5. Examples: What does a "good" answer look like?
  6. Followup: How will I refine this?

Mastering this framework is the real art and science of working with modern LLMs. It shifts your role from simply asking a question to actively directing the intelligence.