Generate Function

Functions that generate text, images, or other content using language models.

fundamentals beginner Jan 28, 2025

What is a Generate Function?

A generate function is a component that takes input (like a prompt or context) and produces output content using a language model or generative AI system. It’s the core mechanism for creating new content in AI applications.

Key Components

Input (Prompt)

  • User query or instruction
  • Context or background information
  • System instructions
  • Examples (few-shot learning)

Model

  • Language Model (GPT, Claude, Llama, etc.)
  • Image Generator (DALL-E, Stable Diffusion)
  • Code Generator (Codex, CodeLlama)

Output

  • Generated text
  • Structured data (JSON, XML)
  • Code snippets
  • Creative content

Basic Structure

def generate(prompt, context=None, parameters=None):
    """
    Generate content using a language model

    Args:
        prompt: User input or instruction
        context: Additional context or background
        parameters: Model parameters (temperature, max_tokens, etc.)

    Returns:
        Generated content as string
    """
    full_prompt = build_prompt(prompt, context)
    response = model.complete(full_prompt, **parameters)
    return response.text

Parameters

Temperature

Controls randomness in generation:

  • Low (0.0-0.3): Deterministic, focused
  • Medium (0.4-0.7): Balanced
  • High (0.8-1.0): Creative, diverse

Max Tokens

Maximum length of generated output

Top-p (Nucleus Sampling)

Probability threshold for token selection

  • 0.9-0.95: Most common for balanced output

Stop Sequences

Tokens that signal end of generation

Example Usage

Simple Generation

from openai import OpenAI

client = OpenAI()

def generate(prompt, temperature=0.7, max_tokens=500):
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        temperature=temperature,
        max_tokens=max_tokens
    )
    return response.choices[0].message.content

# Usage
result = generate("Explain quantum computing in simple terms")

Structured Generation

def generate_json(prompt, schema):
    """Generate structured JSON output"""
    system_prompt = f"""Generate valid JSON matching this schema:
{schema}
Only return JSON, no other text."""

    response = generate(
        prompt=prompt,
        system=system_prompt,
        temperature=0.2  # Lower temp for structured output
    )
    return json.loads(response)

# Usage
schema = {"name": "string", "age": "number", "city": "string"}
result = generate_json("Create a person profile", schema)

Best Practices

Prompt Engineering

  • Clear instructions
  • Provide examples
  • Specify format
  • Set constraints

Error Handling

def safe_generate(prompt, max_retries=3):
    for attempt in range(max_retries):
        try:
            return generate(prompt)
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            continue

Caching

  • Cache common prompts
  • Store expensive generations
  • Use semantic similarity for retrieval

Cost Optimization

  • Use smaller models when possible
  • Limit max_tokens appropriately
  • Batch requests when available
  • Cache results

Common Patterns

Chain of Thought

def generate_with_reasoning(question):
    prompt = f"""Question: {question}

Let's think step by step:
1. First, identify the key information
2. Then, reason through the problem
3. Finally, provide the answer

Answer:"""
    return generate(prompt, temperature=0.3)

Few-Shot Learning

def generate_with_examples(task, examples, input):
    prompt = f"Task: {task}\n\n"
    for ex in examples:
        prompt += f"Input: {ex['input']}\nOutput: {ex['output']}\n\n"
    prompt += f"Input: {input}\nOutput:"
    return generate(prompt, temperature=0.5)

Streaming Generation

For real-time output:

def generate_stream(prompt):
    stream = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        stream=True
    )

    for chunk in stream:
        if chunk.choices[0].delta.content:
            yield chunk.choices[0].delta.content

Key Takeaways

  • Generate functions are the interface to AI models
  • Parameters control output characteristics
  • Proper prompting is crucial for quality
  • Consider cost, latency, and reliability
  • Structure prompts for desired output format
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