When NOT to Use Generative AI

Generative AI like GPT-4 is incredibly capable, but knowing when not to use it is just as important as knowing when to use it.

Red Flags for GenAI

1. You Need Deterministic Outputs

If your application requires exact, predictable responses every time, generative AI isn't the answer. Use rules-based systems or traditional programming.

Example: Banking transaction validation, regulatory compliance checks

2. Accuracy Must Be 100%

LLMs hallucinate. If a single error is unacceptable, don't use pure generative AI. Consider hybrid approaches with validation layers.

Example: Legal contract generation without review, medical dosage calculations

3. You Have Structured Data Problems

If you're working with structured data (spreadsheets, databases), traditional ML often performs better and costs less.

Example: Time series forecasting, classification with labeled training data

4. Real-Time Performance is Critical

API-based LLMs add latency (200-2000ms). For <50ms requirements, on-device or traditional models are better.

Example: High-frequency trading, real-time game AI

5. Cost at Scale is Prohibitive

LLM API costs scale with tokens. At high volume, the economics may not work.

Example: Processing millions of short texts daily

Better Alternatives

Traditional ML

Rules-Based Systems

Hybrid Approaches

When GenAI Shines

The key is matching the tool to the problem, not forcing every problem to fit your favorite tool.