#Prompt

The prompt keyword is one of AIScript's core AI integration features, allowing you to easily interact with large language models (LLMs) directly from your code.

#Basic Usage

The simplest form of the prompt keyword takes a string argument and returns the model's response:

let answer = prompt "What is the capital of France?";
print(answer); // The capital of France is Paris.

In this basic form, AIScript will use the default model configuration (typically OpenAI's model) to generate the response.

#Advanced Configuration

For more control over the prompt behavior, you can provide additional configuration options:

let detailed_answer = prompt {
    input: "Explain quantum computing",
    model: "claude-3.7",
    max_tokens: 500,
    temperature: 0.7,
    system_prompt: "You are a quantum physics expert"
};

#Configuration Options

#Dynamic Prompts

You can construct prompts dynamically using variables and string interpolation:

let topic = "quantum computing";
let detail_level = "beginner";
let response = prompt f"Explain {topic} at a {detail_level} level";

#API Keys and Configuration

To use the prompt feature, you need to configure your API keys. AIScript supports multiple LLM providers including cloud-based services and local models.

#Environment Variables

Set your API keys using environment variables:

# For OpenAI
export OPENAI_API_KEY="your_openai_key"

# For Anthropic Claude
export CLAUDE_API_KEY="your_claude_key"

# For Ollama (local models)
export OLLAMA_API_ENDPOINT="http://localhost:11434/v1"

#Configuration File

Alternatively, you can specify API keys in your project configuration file:

[ai.openai]
api_key = "YOUR_OPENAI_API_KEY"

[ai.anthropic]
api_key = "YOUR_CLAUDE_API_KEY"

# For Ollama (local models)
[ai.ollama]
api_endpoint = "http://localhost:11434/v1"
model = "llama3.2" # or any other model installed in your Ollama instance

#Local Models with Ollama

AIScript supports running local models through Ollama, which allows you to run AI models on your own hardware without sending data to external services.

#Setting Up Ollama

1. Install Ollama from ollama.ai
2. Pull models you want to use:

   ollama pull llama3.2
   ollama pull deepseek-r1
   ollama pull codellama
3. Start Ollama (it runs on port 11434 by default)
4. Configure AIScript to use Ollama

#Example with Local Models

// Using a local Llama model
let response = prompt {
    input: "What is rust?",
    model: "llama3.2"
};

// Using a local coding model
let code_explanation = prompt {
    input: "Review this Python function for potential issues",
    model: "codellama"
};

#Error HandlingNot supported yet

Like other AIScript operations, you can use error handling with the prompt keyword:

let response = prompt "Generate a complex response" |err| {
    print(f"Error occurred: {err}");
    return "Fallback response";
};

#Best Practices

• Be specific: The more specific your prompt, the better the response quality.
• Consider context limits: Each model has a maximum context window. Very large prompts may be truncated.
• Handle rate limits: API providers have rate limits. Consider implementing retry logic for production applications.
• Manage costs: Using LLMs incurs costs based on token usage. Monitor usage to avoid unexpected charges.

#Examples

#Simple Question-Answering

let question = "What is the capital of Japan?";
let answer = prompt question;
print(answer);

#Generating Content with Specific Parameters

let poem = prompt {
    input: "Write a haiku about programming",
    temperature: 0.9,
    max_tokens: 100
};
print(poem);

#Using Different Models

// Using OpenAI
let openai_response = prompt {
    input: f"Summarize this article: {article_text}",
    model: "gpt-4"
};

// Using Claude
let claude_response = prompt {
    input: f"Summarize this article: {article_text}" ,
    model: "claude-3.7"
};

// Using Ollama (local models)
let local_response = prompt {
    input: f"Summarize this article: {article_text}",
    model: "llama3.2"
};

#Chaining Prompts

let topic = prompt "Suggest a topic for a blog post";
let outline = prompt f"Create an outline for a blog post about: {topic}";
let introduction = prompt f"Write an introduction for a blog post with this outline: {outline}";

print("Topic:", topic);
print("Outline", outline);
print("Introduction:", introduction);

#Notes and Limitations

• The response from a prompt is always a string. If you need structured data, consider asking for specific formats like JSON in your prompt or parse the result.
• Network connectivity is required for the prompt keyword to function, as it makes API calls to LLM providers.
• API costs apply based on your provider's pricing model, typically based on the number of tokens processed.