2 posts tagged with "production-ai"

Most teams reach for fine-tuning too early or too late. The ones who fine-tune too early burn weeks on a training pipeline before realizing a better system prompt would have solved the problem. The ones who wait too long run expensive 70B inferences on millions of repetitive tasks while accepting accuracy that a fine-tuned 7B model could have beaten—at a tenth of the cost.

The decision is not about which technique is "better." It's about matching the right tool to your specific constraints: data volume, latency budget, accuracy requirements, and how stable the task definition is. Here's how to think through it.

Most teams building AI agents treat the model as a fixed artifact. You pick a foundation model, write your prompts, wire up some tools, and ship. If the agent starts making mistakes, you tweak the system prompt or switch to a newer model. Learning, in this framing, happens upstream—at the AI lab, during pretraining and RLHF—not in your stack.

This is the wrong mental model. Agents that improve over time do so at three distinct architectural layers, and only one of them involves touching model weights. Teams that understand this distinction build systems that compound in quality; teams that don't keep manually patching the same failure modes.