Skip to content
Mastering Claude

Home

Getting good work out of Claude5 lessons33 min

Choosing your model and your tool

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.