Automatic Model Selection for Improved Efficiency and Sustainability

ChatGPT
1

My name is Ivan, a ChatGPT Pro user and Sustainable Cities and Urban AI professional.

I am reaching out to propose an automatic model-selection layer in ChatGPT, designed to route queries to the most suitable model based on prompt complexity.

Rationale

  • User Simplicity: Most users, including myself, often default to GPT-4 for all queries—even simple ones—thereby incurring unnecessary computational cost and latency. An automated system frees the user from manually choosing a model and helps sustain responsive service quality.

  • Operational Efficiency: Reserving GPT-4 primarily for complex or multi-step questions can reduce load on expensive resources and cut API costs.

  • Environmental Impact: By preventing overuse of GPT-4, we can lower overall energy consumption and associated carbon emissions—an approach increasingly aligned with corporate sustainability targets.

Proposed Approach

  1. Prompt Classification: A small classifier (rule-based or ML-driven) evaluates each user prompt.

  2. Model Routing: If the prompt is simple, it routes to GPT-3.5; moderate queries go to a mid-tier (e.g., GPT-4o), and truly complex prompts use GPT-4.

  3. Fallback Logic: If the smaller model lacks confidence or the user is unsatisfied, ChatGPT can escalate automatically.

Example Implementation (for illustration purposes, code does not have valid API keys, and uses place holder model names)

python

import re
import openai

Set your API key, or ensure it’s set in your environment

openai.api_key = “YOUR_OPENAI_API_KEY”

def classify_prompt(prompt: str) → str:
# Normalize prompt to lowercase for analysis
text = prompt.lower()
tokens = len(text.split())

# Heuristic 1: keyword patterns indicating complexity
complex_indicators = [
    r"explain|analyze|describe in detail",  # asks for explanation/analysis
    r"step by step|provide a detailed",     # multi-step answer likely needed
    r"calculate|prove|derive",              # indicates math or logical proof
    r"compare|contrast|advantages|disadvantages",  # comparative analysis
]

# Heuristic 2: keyword patterns for simple queries
simple_indicators = [
    r"what is|who is|define",  # definition or factual
    r"summary|summarize",      # summarization
    r"example of|why is",      # basic explanation
]

# Check for complex indicators
for pattern in complex_indicators:
    if re.search(pattern, text):
        return "complex"

# Check for simple indicators
for pattern in simple_indicators:
    if re.search(pattern, text):
        # If found a simple indicator and not too long, label simple
        if tokens < 50:
            return "simple"

# Fallback based on length or unknown patterns
if tokens > 100:
    return "complex"   # very long prompt => likely complex or needs more context
elif tokens > 30:
    return "moderate"  # medium-length prompt with no clear keywords
else:
    return "simple"

def route_and_respond(prompt: str) → str:
# Determine complexity
level = classify_prompt(prompt)

# Select model based on level
if level == "simple":
    model_name = "gpt-3.5-turbo"
elif level == "moderate":
    model_name = "gpt-4"
else:  # "complex"
    model_name = "gpt-4"

# Prepare the API call
response = openai.ChatCompletion.create(
    model=model_name,
    messages=[{"role": "user", "content": prompt}]
)
answer = response['choices'][0]['message']['content']
return answer

Conclusion

By adding a straightforward classification-and-routing step, ChatGPT could more efficiently allocate computational resources, offering:

  • Faster responses for simple queries

  • Cost savings and better scaling for OpenAI’s infrastructure

  • Meaningful reduction in carbon footprint

Thank you for your time, and for all that you and your colleagues do in pushing AI innovation forward.
Please let me know how can I contribute to OpenAI’s work on smart cities.

Best regards,
Ivan Tamayo Ramos