ChatGPT API Prompting

Best practices for integrating ChatGPT into applications with optimized prompting, error handling, and cost efficiency.

ChatGPT API Prompting Overview

Integrating ChatGPT via API requires optimized prompting strategies that balance response quality, cost, and latency. Production-grade prompts differ from chat-based prompts: they must handle edge cases, maintain consistency, and work reliably at scale.

Key Considerations:

✓ Deterministic, reproducible responses
✓ Token optimization and cost management
✓ Error handling and fallback strategies
✓ Rate limiting and retry logic
✓ Prompt caching for frequently-used instructions
✓ Function calling for structured outputs
✓ Streaming for real-time user feedback

Optimal Prompt Structure for API

System + User Message Pattern

System Message (Cached):
You are a [ROLE/PURPOSE]. Your task is to [SPECIFIC GOAL].

RULES:
- Output format: [JSON/CSV/MARKDOWN]
- Constraints: [SPECIFIC LIMITS]
- Edge cases: [HOW TO HANDLE]
- Tone: [STYLE]

User Message (Input):
[USER DATA/QUERY]

Example output:
[SHOW EXPECTED FORMAT]

This structure is cached on the API, reducing cost for repeated requests.

Function Calling Best Practices

Defining Function Schemas

functions = [
  {
    "name": "analyze_sentiment",
    "description": "Analyze sentiment of customer feedback",
    "parameters": {
      "type": "object",
      "properties": {
        "text": {"type": "string"},
        "scale": {"type": "integer", "minimum": 1, "maximum": 10}
      },
      "required": ["text", "scale"]
    }
  }
]

# Call API with function definitions
response = openai.ChatCompletion.create(
  model="gpt-4-turbo",
  messages=[{"role": "user", "content": user_input}],
  functions=functions,
  function_call="auto"  # Let model decide when to call
)

Cost Optimization Strategies

1. Prompt Caching

Cache long system prompts to reduce token costs by 90% on repeated calls.

# System message marked for caching
system_message = {
  "role": "system",
  "content": [...large instruction set...],
  "cache_control": {"type": "ephemeral"}
}

2. Model Selection

Use GPT-4 Turbo for complex tasks, GPT-3.5 Turbo for simple ones. Estimate 10x cost difference.

3. Token Optimization

Remove unnecessary words, use shorthand for common terms, return only needed fields in output.