Leveraging LLMs as Functions for Dynamic Code Generation

Leveraging LLMs as Functions for Dynamic Code Generation

Language models (LLMs) have become incredibly versatile, extending beyond their traditional role in text generation. One fascinating application is using LLMs to implement functions within a program-what I call "LLM As A Function." This approach leverages the power of language models to generate code dynamically based on specific inputs and constraints.

Example: Building a Website Builder

Let's consider a practical example where you want to build a website builder that uses only components from your design system. Here’s how you can achieve this using an LLM:

const prompt = 'Build a chat app UI';

// Step 1: Determine the required components
const components = llm<Array<string>>(`
  You only have the following components: ${designSystem.getAllExistingComponents().join(', ')}\n
  What components do you need to build the following:\n
  ${prompt}
`);
// Output: ['List', 'Card', 'ProfilePicture', 'TextInput']

// Step 2: Generate the code for these components
const result = llm<{javascript: string, css: string}>(`
  You only have the following components: ${components.join(',')}\n
  Here are examples of how to use them:\n
  ${components.map(component => designSystem.getExamplesForComponent(component).join('\n')).join('\n')}\n
  Write code for making the following:\n
  ${prompt}
`);
// Output: { javascript: '...', css: '...' }

How It Works

The llm<Type>(prompt: string): Type function is key here. It takes a type definition and a prompt, then returns a value of that type. Here’s a breakdown of the process:

• Type Definition: The function requires an explicit type for the output. For example, in the second call, the type is {javascript: string, css: string}.
• Introspection and Code Generation: To handle this, you need introspection or code generation capabilities in your programming language to manipulate the type definition.
• JSON Example Augmentation: The function converts the type to a JSON example and appends it to the prompt. For instance, if the type is {javascript: string, css: string}, the augmented prompt might look like:

  You need to respond using JSON that looks like {"javascript": "...", "css": "..."}
  

Benefits and Use Cases

• Dynamic Code Generation: LLMs can generate code on the fly based on specific requirements, making development more flexible.
• Type Safety: By defining explicit types, you ensure that the generated output is structured correctly, reducing errors.
• Component Constraints: You can enforce constraints (like using only certain components) by including them in the prompt, ensuring consistency with your design system.

Implementation Details

To implement this approach, you need:

• Language Model API: Access to a powerful LLM like ChatGPT or Anthropic's Claude.
• Type System Integration: A way to convert type definitions into JSON examples and integrate them into the prompt.
• Prompt Engineering: Crafting prompts that guide the model to generate the desired output accurately.

Example in Action

Here’s a more detailed look at how the llm function might be implemented:

function llm<Type>(prompt: string): Type {
  // Convert type to JSON example
  const typeExample = convertTypeToJsonExample(Type);

  // Augment prompt with type information
  const augmentedPrompt = `${typeExample}\n${prompt}`;

  // Call the LLM API
  const response = callLlmApi(augmentedPrompt);

  // Parse the response and return it as the specified type
  return JSON.parse(response) as Type;
}

function convertTypeToJsonExample(Type: any): string {
  // Example implementation for simple types
  if (Type === Array<string>) {
    return 'You need to respond using a JSON array of strings like ["item1", "item2"]';
  } else if (Type === {javascript: string, css: string}) {
    return 'You need to respond using JSON that looks like {"javascript": "...", "css": "..."}';
  }
  // Add more type handling as