Fine-Tuning vs. RAG vs. Prompt Engineering: How to Choose
Introduction With open-weight models like Gemma 4, Qwen3, and Trinity-Large-Thinking now downloadable and fully customizable, developers face a decision that used to
Long-form explainers and evergreen references you can return to.
Introduction With open-weight models like Gemma 4, Qwen3, and Trinity-Large-Thinking now downloadable and fully customizable, developers face a decision that used to
Introduction A model scores 80% on a medical image benchmark. Impressive — except researchers at Stanford found frontier models like GPT-5, Gemini 3
Introduction Running LLMs in production is expensive. Sora reportedly burns through roughly $1M per day in compute. ScaleOps just raised $130M at
AI agents are graduating from demos to production infrastructure. They call APIs, read databases, write files, push commits, and spawn subagents — often
AI agents are no longer a research curiosity. Frameworks are maturing, patterns are solidifying, and the gap between "impressive demo"
Single-hop RAG is a solved problem. You chunk documents, embed them, retrieve the top-k, and stuff them into a prompt. It works
Building an agent is the easy part. Knowing whether it actually works in production is the field's biggest unsolved problem.
The Claude API has matured considerably. What was once a straightforward text-in, text-out endpoint now encompasses streaming, tool use, a 1M token
Prompt engineering has a reputation problem it mostly deserves. For two years, it was treated as a magic incantation discipline — sprinkle "