fine-tuning
Your team wants to fine-tune a model for better performance. The estimated cost: $30-50K in data prep, training, and infrastructure. But you could get 80% of the benefit from better prompts and evals for $5K. Here's how to know which path actually matters.
July 23, 2026 · 8 min read
prompt engineering
Your prompt works perfectly in testing. Then it ships to production and suddenly fails on 15% of real requests. The problem isn't the model. It's that you tuned your prompt to development data, not to the chaos of actual user inputs.
July 17, 2026 · 8 min read
