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
AI cost
You budgeted for the model. Then came inference costs, fine-tuning, infrastructure, ops overhead, and the team time to keep it running. Most mid-market operators are shocked by month two. Here's the breakdown.
July 16, 2026 · 7 min read
data quality
Mid-market operators invest in AI models but get inconsistent results. The problem is almost never the LLM. It's what you're feeding it: incomplete, duplicated, or misstructured data that makes any model look bad. Here's how to fix it before you buy another tool.
July 15, 2026 · 8 min read
