For the past three years, the dominant paradigm in AI has been \"bigger is better.\" But if you are building an application today—whether it's a customer support chatbot or an automated coding assistant—massive models are often overkill. They are slow, expensive, and prone to unpredictable hallucinations. \n\nSmall Language Models (7B to 14B parameters like Llama 3 or Mistral) are dominating the developer space because of:\n1. Cost Efficiency\n2. Low Latency \n3. Fine-Tuning Superiority (fine-tuning an 8B model on proprietary data for a specific task often outperforms GPT-4 on that specific task).\n\nThe Playbook: Stop trying to prompt-engineer a massive model. Generate a high-quality dataset of 1,000 examples using a massive model, and use that dataset to fine-tune a small, open-source model. \n\nIf you found this helpful, I write a weekly newsletter for AI builders covering deep dives like this, new models, and tools.