Prompting Fundamentals

Now that you've got a basic understanding of LLMs and how they serve as an API we can dive into the secret sauce - how to actually speak the language of these models to get the results that you want.

You do this using prompts.

Prompts are the text input that you send to the LLM. Prompting can be powerful, but requires effective techniques to get consistent results. An LLM will respond to any prompt, but all prompts are not created equally.

Good prompts can turn an LLM from novelty into a reliable coworker.

Think of prompting a model like a chef preparing a meal. Bad ingredients will result in a bad meal.

Same with AI: bad prompt = bad output, no matter how fancy your code wrapper.

A good prompt is crucial. It's what gets the AI to consistently do what you want.

🔄 The Golden Rule of Prompting

Iterate aggressively. Monitor outputs. Keep tweaking.

Before diving into techniques, understand the basic anatomy of a good prompt:

Basic Prompt Structure (ICOD). Good prompts typically contain:

Instruction: What task to do

Context: Background info

Output Indicator: Format requirements (critical for generateObject)

Data: The actual input

3 Techniques for Prompt Engineering

Let's dive into three core techniques every builder needs to know:

Zero-Shot: Just ask directly without examples
Few-Shot: Provide examples to guide the output format
Chain-of-Thought: Break complex problems into steps

Zero shot image

Zero-Shot Prompting: Just Ask!

This is the simplest and most common form of prompting: simply asking the model to do something directly, without providing examples.

Example (Conceptual):
- Prompt: Classify the sentiment (positive/negative/neutral): 'This movie was okay.'
- Expected Output: Neutral
AI SDK Context: Great for simple generateText calls where the task is common (like basic summarization, Q&A). Relies heavily on the model's pre-trained knowledge.

javascript

This approach is great for quick for straightforward tasks, but less reliable for complex instructions or specific output formats.

Few-Shot Prompting: Show, Don't Just Tell

For more complex tasks or specific output formats, you need to provide examples within the prompt to show the model the pattern or format you want it to follow.

Example (Fictional Word):

text

The model sees the pattern (definition → example) and completes it. This structured approach uses our ICOD framework:

ICOD Breakdown for Few-Shot Example

Instruction: Implied - complete the pattern for the new word
Context: The examples showing definition-to-example pattern
Output Indicator: Format shown in examples (Word Example: ...)
Data: The new word "Vardudel" and its definition

javascript

Providing examples massively improves reliability for specific formats. Clear labels and consistent formatting in examples are key!

Chain-of-Thought (CoT) Prompting: Think Step-by-Step

Mimic human problem-solving by prompting the model to "think out loud" and break down a complex task into intermediate reasoning steps before giving the final answer.

Example (Odd Numbers Sum):

text

Here's how you would use this style of prompt with the AI SDK:

javascript

Showing the model "how to think" about the problem improves reliability for logic and complex reasoning. Combine this with few-shot. Remember that this technique often performs best with more capable models.

Core Prompting Advice for Builders

Remember this crucial advice:

Be Realistic: Don't try to build Rome in a single prompt. Break complex application features into smaller, focused prompts for the AI SDK functions.
Be Specific & Over-Explain: Define exactly what you want and don't want. Ambiguity leads to unpredictable results.
Remember the Golden Rule: Iterate aggressively. Nothing's perfect on the first try - keep testing and refining!

Ricky Bobby from Talladega Nights saying 'I'm not sure what to do with my hands'

Don't leave the model wondering what to do with its hands!

Practice in the AI SDK Playground

Before setting up your local environment, let's practice these prompting techniques using the AI SDK Playground. This web-based tool lets you experiment with prompts immediately - no setup required!

The playground allows you to:

Compare different prompts and models side-by-side
Adjust parameters like temperature and max tokens
Save and share your experiments
Test structured output with schemas

Why This Practice Matters

The AI SDK Playground lets you experiment with prompting techniques immediately. You're learning patterns that will power the generateObject and generateText calls you'll build in upcoming lessons.

Key insight: Good prompts + structured schemas = reliable AI features in your applications!

Exercise 1: Few-Shot Prompting Practice

Open the AI SDK Playground and try this Few-Shot example:

Prompt to try:

text

What to observe:

How the examples guide the AI to follow the same format
The consistency of categorization when you have clear patterns
Try removing the examples and see how the output changes

Exercise 2: Chain-of-Thought Exploration

In the playground, test this Chain-of-Thought prompt:

Prompt to try:

text

What to observe:

How step-by-step reasoning improves complex problem solving
The difference in quality compared to a direct answer
Try the same question without the Chain-of-Thought structure

Exercise 3: Schema-Guided Structured Output

Switch to structured output mode in the playground and test this schema:

Schema:

text

Prompt: "Analyze this user feedback: 'Love the new search feature, but it's a bit slow when I type fast.'"

Next Step: Setting Up Your AI Dev Environment

You've grasped the core prompting techniques and practiced implementing them with the AI SDK. Now it's time to prepare your local machine and set up your development environment with the necessary tools and API keys.

The best way to solidify your prompting skills is by building real stuff. Let's get your environment ready so you can go from talking about prompts to implementing them in working code.

Now that you've got a basic understanding of LLMs and how they serve as an API we can dive into the secret sauce - how to actually speak the language of these models to get the results that you want.

You do this using prompts.

Good prompts can turn an LLM from novelty into a reliable coworker.

Think of prompting a model like a chef preparing a meal. Bad ingredients will result in a bad meal.

Same with AI: bad prompt = bad output, no matter how fancy your code wrapper.

A good prompt is crucial. It's what gets the AI to consistently do what you want.

🔄 The Golden Rule of Prompting

Iterate aggressively. Monitor outputs. Keep tweaking.

Before diving into techniques, understand the basic anatomy of a good prompt:

Basic Prompt Structure (ICOD). Good prompts typically contain:

Instruction: What task to do

Context: Background info

Output Indicator: Format requirements (critical for generateObject)

Data: The actual input

3 Techniques for Prompt Engineering

Let's dive into three core techniques every builder needs to know:

Zero-Shot: Just ask directly without examples
Few-Shot: Provide examples to guide the output format
Chain-of-Thought: Break complex problems into steps

Zero shot image

Zero-Shot Prompting: Just Ask!

This is the simplest and most common form of prompting: simply asking the model to do something directly, without providing examples.

Example (Conceptual):
- Prompt: Classify the sentiment (positive/negative/neutral): 'This movie was okay.'
- Expected Output: Neutral
AI SDK Context: Great for simple generateText calls where the task is common (like basic summarization, Q&A). Relies heavily on the model's pre-trained knowledge.

javascript

This approach is great for quick for straightforward tasks, but less reliable for complex instructions or specific output formats.

Few-Shot Prompting: Show, Don't Just Tell

For more complex tasks or specific output formats, you need to provide examples within the prompt to show the model the pattern or format you want it to follow.

Example (Fictional Word):

text

The model sees the pattern (definition → example) and completes it. This structured approach uses our ICOD framework:

ICOD Breakdown for Few-Shot Example

Instruction: Implied - complete the pattern for the new word
Context: The examples showing definition-to-example pattern
Output Indicator: Format shown in examples (Word Example: ...)
Data: The new word "Vardudel" and its definition

javascript

Providing examples massively improves reliability for specific formats. Clear labels and consistent formatting in examples are key!

Chain-of-Thought (CoT) Prompting: Think Step-by-Step

Mimic human problem-solving by prompting the model to "think out loud" and break down a complex task into intermediate reasoning steps before giving the final answer.

Example (Odd Numbers Sum):

text

Here's how you would use this style of prompt with the AI SDK:

javascript

Core Prompting Advice for Builders

Remember this crucial advice:

Be Realistic: Don't try to build Rome in a single prompt. Break complex application features into smaller, focused prompts for the AI SDK functions.
Be Specific & Over-Explain: Define exactly what you want and don't want. Ambiguity leads to unpredictable results.
Remember the Golden Rule: Iterate aggressively. Nothing's perfect on the first try - keep testing and refining!

Ricky Bobby from Talladega Nights saying 'I'm not sure what to do with my hands'

Don't leave the model wondering what to do with its hands!

Practice in the AI SDK Playground

The playground allows you to:

Compare different prompts and models side-by-side
Adjust parameters like temperature and max tokens
Save and share your experiments
Test structured output with schemas

Why This Practice Matters

The AI SDK Playground lets you experiment with prompting techniques immediately. You're learning patterns that will power the generateObject and generateText calls you'll build in upcoming lessons.

Key insight: Good prompts + structured schemas = reliable AI features in your applications!

Exercise 1: Few-Shot Prompting Practice

Open the AI SDK Playground and try this Few-Shot example:

Prompt to try:

text

What to observe:

How the examples guide the AI to follow the same format
The consistency of categorization when you have clear patterns
Try removing the examples and see how the output changes

Exercise 2: Chain-of-Thought Exploration

In the playground, test this Chain-of-Thought prompt:

Prompt to try:

text

What to observe:

How step-by-step reasoning improves complex problem solving
The difference in quality compared to a direct answer
Try the same question without the Chain-of-Thought structure

Exercise 3: Schema-Guided Structured Output

Switch to structured output mode in the playground and test this schema:

Schema:

text

Prompt: "Analyze this user feedback: 'Love the new search feature, but it's a bit slow when I type fast.'"

Next Step: Setting Up Your AI Dev Environment

The best way to solidify your prompting skills is by building real stuff. Let's get your environment ready so you can go from talking about prompts to implementing them in working code.

Builders Guide to the AI SDK

3 Techniques for Prompt Engineering

Zero-Shot Prompting: Just Ask!

Few-Shot Prompting: Show, Don't Just Tell

Example (Fictional Word):

Chain-of-Thought (CoT) Prompting: Think Step-by-Step

Example (Odd Numbers Sum):

Core Prompting Advice for Builders

Practice in the AI SDK Playground

Exercise 1: Few-Shot Prompting Practice

Exercise 2: Chain-of-Thought Exploration

Exercise 3: Schema-Guided Structured Output

Further Reading (Optional)

Next Step: Setting Up Your AI Dev Environment

Prompting Fundamentals

3 Techniques for Prompt Engineering

Zero-Shot Prompting: Just Ask!

Few-Shot Prompting: Show, Don't Just Tell

Example (Fictional Word):

Chain-of-Thought (CoT) Prompting: Think Step-by-Step

Example (Odd Numbers Sum):

Core Prompting Advice for Builders

Practice in the AI SDK Playground

Exercise 1: Few-Shot Prompting Practice

Exercise 2: Chain-of-Thought Exploration

Exercise 3: Schema-Guided Structured Output

Further Reading (Optional)

Next Step: Setting Up Your AI Dev Environment