Basic Prompting

What is Prompting?

Prompting is the art and science of communicating with AI models to get the results you want. Think of it as giving instructions to a highly capable assistant.

Why Prompting Matters

The same AI model can produce vastly different outputs based on how you prompt it. Let's see this in action:

Notice how the specific prompt yields more actionable and relevant information? That's the power of good prompting!

Key Principles

1. Clarity is King

Be explicit about what you want. AI models can't read your mind. The more specific you are about your requirements, the better the AI can tailor its response to your needs.

Notice how the good prompt specifies the target audience (beginners), the topic (Python functions), and the expected format (3 examples)? This level of detail helps the AI understand exactly what you're looking for.

2. Context Matters

Provide relevant background information to guide the AI's response. Without context, the AI has to guess what you need, which often leads to generic or unhelpful answers.

The good prompt includes the programming language, the type of error, what the code does, and when the error occurs. This context enables the AI to provide a targeted solution rather than generic debugging advice.

3. Format Your Output

Specify how you want the information presented. Different formats serve different purposes—bullet points for quick scanning, paragraphs for detailed explanations, tables for comparisons. By explicitly stating your format preferences, you ensure the output matches your intended use case.

This example demonstrates how format specifications can be combined with other requirements (like target audience and length) to create a well-structured prompt that produces exactly the kind of output you need.

Common Pitfalls

Too Vague

If you write a prompt like "Help me with my project," you're not giving the AI enough detail to provide a meaningful or useful response. The AI doesn't know what your project is about, what kind of help you need, or what your goals are. Always specify the context, your objectives, and what you expect in the response.

Too Complex

When you try to ask for too many things at once—for example, giving a prompt that contains ten different questions or requests—the AI may only address some of them or provide shallow, incomplete answers. For best results, break up complex tasks into separate, focused prompts so each request is addressed thoroughly.

Assuming Context

The AI doesn't have memory of your previous conversations unless you explicitly include that information in your prompt. If you refer to "the document I sent earlier" or make assumptions about shared history, the AI will not know what you mean. Always restate necessary details or reference earlier parts of the conversation if you want them to be considered.

Anatomy of a Good Prompt

Understanding the components of effective prompts will help you craft better instructions every time.

The SCOPE Framework

Structure prompts using these essential components:

Situation

Establish the working environment and circumstances. This sets the stage for everything that follows, helping the AI understand the context in which it's operating.

By starting with the situation, you immediately orient the AI to your specific context—in this case, a business presentation scenario with a particular audience.

Capability

Define the expertise level and perspective needed. This helps the AI adopt the right "voice" and draw from the appropriate knowledge base for your task.

Specifying the capability ensures the AI responds with the depth and perspective of someone with that expertise, rather than providing a generic answer.

Objective

State your specific goal and desired outcome. This is the "what" of your prompt—the core task you want the AI to accomplish.

A clear objective helps the AI prioritize what's most important in its response, ensuring it stays focused on your primary goal.

Parameters

Set constraints, requirements, and boundaries. These guardrails ensure the output fits within your practical limitations and meets your specific needs.

Parameters help prevent the AI from going off-track or producing something that doesn't fit your constraints, whether those are length, style, complexity, or other factors.

Expectation

Clarify the format, style, and delivery method. This final component ensures you receive the output in exactly the form you need to use it effectively.

By specifying expectations upfront, you avoid the need for reformatting or style adjustments after receiving the initial response, saving time and ensuring consistency.

Complete Example

Let's put it all together:

Not every prompt needs all five SCOPE elements. Use what's relevant for your specific use case. Simple queries might only need Objective and Expectation.

Common Prompt Patterns

These battle-tested patterns will help you achieve consistent results across different use cases.

The Persona Pattern

Make the AI adopt a specific expertise or perspective. This pattern is powerful because it leverages the AI's training on diverse knowledge domains, allowing it to respond with the depth and nuance of an expert in that field.

Notice how the persona (Michelin-starred chef) combined with the situation (home cook, dinner guests) creates a prompt that balances sophistication with practicality. The AI will draw on culinary expertise while keeping the suggestions achievable for the target audience.

The Step-by-Step Pattern

Break down complex tasks into manageable steps. This pattern is especially useful when you need structured, sequential instructions that are easy to follow and implement.

This pattern works great for tutorials, how-to guides, and troubleshooting. By explicitly requesting numbered steps with prerequisites and potential pitfalls, you ensure the output is actionable and anticipates common issues users might encounter.

The Comparison Pattern

Analyze multiple options to make informed decisions. This pattern helps you evaluate trade-offs and understand the relative strengths and weaknesses of different approaches, tools, or solutions.

The comparison pattern is most effective when you specify the criteria that matter to your specific use case. In this example, the criteria (learning curve, community support, performance, ecosystem) are all relevant to a startup's decision-making process, and the table format makes it easy to scan and compare options side-by-side.

The Question Refinement Pattern

Get the AI to help you ask better questions. This meta-pattern is particularly valuable when you're exploring a new domain and don't yet know what you need to know.

This pattern creates a learning roadmap by generating questions that build on each other. Starting with beginner questions and progressing to expert-level ones helps you understand both the fundamentals and the advanced concepts you'll need to master the topic.

The Template Pattern

Create reusable prompts with variables. This pattern is essential for building scalable AI applications where you need to generate similar content with different inputs.

Templates allow you to define the structure and style once, then reuse it with different variable values. This ensures consistency across multiple outputs while allowing for customization. The example above shows how placeholders (in brackets) mark where dynamic content will be inserted.

These patterns can be combined! For example, use the Persona + Step-by-Step pattern to get expert-level tutorials, or combine the Comparison Pattern with the Template Pattern to create reusable comparison frameworks.

Prompt Templates & Variables

In product development, you often need to use the same prompt in multiple places. This is where prompt templates come into play. They allow you to reuse the same prompt structure across multiple places, with varying inputs.

When to Use Prompt Templates

Use prompt templates when you expect any part of your prompt to be repeated, or used again with different inputs.

• User Input: When you have user input that needs to be inserted into the prompt.

• Testing: Optimize your testing process by using prompt templates to test different inputs and edge cases.

• Reusability: Reuse the same prompt structure across multiple places, with varying inputs.

Template Structure

Templates use double brackets {{variable_name}} for placeholders, making them easily identifiable and allowing for quick testing of different values. This syntax is widely recognized and works well with most templating systems and AI platforms.

This example demonstrates a complete template with multiple variables: source_language, target_language, and text_to_translate. When you use this template, you'd replace these placeholders with actual values (e.g., "English", "Spanish", and the actual text to translate). The structure remains consistent, but the content adapts to your specific needs.

Advanced Template Patterns

As your templates become more sophisticated, you can incorporate conditional logic and loops. These advanced patterns allow you to create flexible templates that adapt based on your inputs.

This template uses XML-style tags to structure different sections of the prompt, making it easy to parse and modify. The {{#each}} loop allows you to dynamically include multiple criteria, and the {{#if}} conditional means recommendations are only included when needed. This level of structure makes templates more maintainable and easier to understand.

This multi-document template shows how you can handle variable numbers of inputs. The {{#each}} loop processes each document in the array, and each document can have its own metadata (source, type) along with content. The @index variable provides a unique identifier for each document, which can be useful for referencing specific documents in the analysis.

Template Benefits in Practice

Version Control

Track changes to your prompt structure over time by keeping tabs only on the core template, separate from dynamic inputs.

Scalability

Simplify prompt management as your application grows in complexity. Templates make it easy to maintain consistency across different use cases.

Evaluation Ready

Templates work seamlessly with evaluation tools by separating the variable and fixed portions of your prompts for systematic testing.

Try Template Variables

Here's a practical example that demonstrates how template variables work in a real-world scenario. Notice how different variable values change the prompt's behavior while maintaining the same core structure.

Modern AI platforms like Claude Console heavily use prompt templates for features like prompt generators, improvers, and evaluation tools. Learning templates now sets you up for advanced AI development workflows.

Prompting Best Practices

Follow these guidelines to consistently get high-quality responses from AI models.

Golden Rules

These fundamental principles apply to nearly every prompting scenario.

1. Be Specific

Vague prompts produce vague results. The more specific you are, the better the output.

2. Provide Examples

Show the AI what you want. Examples dramatically improve output quality and consistency. When you provide examples, you're essentially training the AI on-the-fly to match your preferred style, tone, and structure.

By providing two examples, the AI can identify the pattern: starting with an engaging benefit statement, highlighting key features, and maintaining a concise, benefit-focused tone. This is far more effective than trying to describe the style in words alone.

3. Iterate and Refine

First attempts are rarely perfect. Use follow-up prompts to improve the output. Think of prompting as a conversation where each exchange helps you get closer to exactly what you need.

This iterative approach allows you to refine the output incrementally. Start broad, then narrow down based on what you receive. Each follow-up builds on the previous response, allowing you to shape the final output without having to craft the perfect prompt from the start.

Advanced Tips

Use Delimiters for Structure

Clearly separate different parts of your prompt using delimiters like triple quotes, XML tags, or markdown headers. Delimiters help the AI distinguish between instructions, input data, examples, and output requirements, reducing ambiguity and improving accuracy.

In this example, the triple quotes clearly mark where the customer review begins and ends, making it obvious to the AI what content needs to be analyzed versus what are the instructions for analysis.

Ask the AI to Think Step-by-Step

For complex reasoning tasks, explicitly request step-by-step thinking. This technique, sometimes called "chain-of-thought" prompting, helps the AI work through problems methodically and often leads to more accurate results, especially for mathematical or logical problems.

By asking for step-by-step work, you not only get the correct answer but also understand the reasoning process. This is particularly valuable when you need to verify the logic or when the problem-solving approach is as important as the final answer.

Use Negative Instructions

Tell the AI what NOT to do to avoid common mistakes. Sometimes it's easier to specify what you want to avoid than to describe everything you do want. Negative instructions are particularly useful for preventing common pitfalls or unwanted behaviors.

Negative instructions act as guardrails, preventing the AI from including elements you specifically want to avoid. Combined with positive instructions, they create a clear boundary for what the output should and shouldn't contain.

Common Mistakes to Avoid

• Overloading with Multiple Tasks: Asking the AI to do 5 different things at once often results in incomplete or shallow responses. Break complex requests into separate prompts.

• Assuming Too Much Context: The AI doesn't have access to your files, previous sessions, or screen unless you explicitly provide that information.

• Ignoring Output Format: Failing to specify how you want the information formatted (JSON, markdown, bullet points, etc.) leads to inconsistent results.

• Not Testing Edge Cases: Always test your prompts with unusual or boundary inputs to see how they perform in real-world scenarios.

Quick Reference Checklist

Before submitting your prompt, verify:

• Is my goal clearly stated?
• Have I provided all necessary context and background?
• Did I specify the desired output format?
• Have I included examples or constraints where appropriate?
• Is the prompt focused on one main task rather than multiple?

Try It Yourself

Put all the best practices together in this comprehensive example. Notice how it combines multiple techniques: a clear persona, specific task definition, detailed parameters, and explicit format requirements. This is what a well-crafted prompt looks like in practice.

Great prompting is like great communication—it's clear, specific, and provides context. Master these practices and you'll see immediate improvements in your AI interactions.