A practical first guide to adding AI to your product. From problem framing to first deployment.
8 min readAI works best when the problem is specific. Identify one high-value, repetitive task where AI can outperform a human or a rule engine.
Most problems need 500–5,000 labelled examples. Start small, validate quickly, then scale data collection.
For Turkish tasks: fine-tuned open models outperform zero-shot GPT-4 at 1/10th the cost. Start with a hosted API before self-hosting.
Split data into train/val/test before touching it. Track accuracy, latency, and cost — not just vibes.
Use a shadow deployment first. Monitor output quality, latency P95, and error rates. Set alerts before going live.