Choosing your model and your tool
- 01The model landscape, one family of predictors7 min
Claude, GPT and Gemini rest on the same basic training principle, which Anthropic describes for Claude as predicting the next word. Open-weight models share this architecture. A prompt technique learned on Claude therefore often works almost as it stands elsewhere, even though the context window, data handling and tools differ.
- 02Route by task, not by brand7 min
Choose a model by what the task requires: capability, speed, cost and tuning effort, not out of habit. Two questions place a task: routine or new, low stakes or high. Tuning the prompt, with a more precise role or better-set context, often changes the result more than switching models does.
- 03Composing a system prompt across several providers7 min
When a prompt has to work across several providers, take each one's strongest rule, merge them without contradiction, and rules written by the prompt's owner generally outrank a provider's defaults, except when they would conflict with the safety limits that provider sets for itself.
- 04Local model, custom-built, or simply a better prompt6 min
For a small organisation, in the vast majority of cases, a better-written prompt solves the problem faster. A local open-weight model involves no cost charged per call and sends no data out, but it takes hardware. A custom-built model costs a lot to construct and is only worth it at very high volume or for a fixed format.
- 05Low, medium, high, xhigh, max: choosing your effort level6 min
Start from the level marked Default, go up one notch if the answer seems superficial, go down one notch if it drags on a simple question. The effort level sets how much work Claude puts in before answering: higher effort gives more thorough answers, but they take longer and use up your plan faster.