# Four Prompt Patterns Worth Testing on Claude (None of Them Is 10x) > Source: > Published: 2026-11-14 03:31:27+00:00 My working title for this post was "The Prompt Pattern That Gets 10x Better Results from Claude." I changed it. I can't back that up. A 10x claim needs a metric. Ten times fewer errors? Ten times cheaper? Without a metric, a multiplier is a slogan, and nobody can check it. What I can show are four patterns Anthropic documents, three of them with published test results. **Scope:** I did not run any benchmarks. The results below are Anthropic-reported, mostly measured on Claude Sonnet 5.5, from its prompting guides, read on 2026-11-10. The prompt wording in the full post is mine and untested. Most prompts say what to do and stop. A better one makes four promises explicit: **task** (what and why), **scope** (where the job ends), **proof** (how the model shows it's done), and **shape** (what the answer looks like). Per Anthropic's Sonnet 5.5 guide, at low and medium effort on long agentic tasks the model sometimes checks in before finishing. At every effort level it tends to add tests, docs, and small files you didn't ask for. At `xhigh` and `max` effort it can also start its own review rounds, sometimes with reviewer subagents. **Anthropic reports that an instruction to stop once the work and its checks pass, with no extra review rounds unless requested, cut session cost by about a third in coding tests at `max` effort, with no change in quality.** Read the fine print: one model, one effort level, one family of tasks, one specific prompt. Anthropic also notes the instruction reduces self-started reviews without removing them entirely. At `low` effort, Sonnet 5.5 sometimes reports a code change as done without running a check that exercises it, for example skipping the tests because dependencies weren't installed. Anthropic reports that its version of a "run a real check" paragraph makes skipped or superficial checks rare at `low` effort, with no measurable quality change and a slightly higher cost per task. The pattern has three parts: require a real check, define what counts as real, and give an honest exit ("say which check you couldn't run and why"). This one is narrow. It applies to **JSON answers on tasks that need a few steps of working out**, like totaling figures or applying a rule. With structured outputs, the model can only work the problem out in its thinking, and it often skips thinking at low and medium effort. Anthropic's fix is one line at the end of the system prompt, used with adaptive thinking: ``` Think the problem through before you answer. ``` Reported results: at `high` effort it brings accuracy close to `xhigh` for a modest token increase. At `low` and `medium` it raises accuracy but not to `high`'s level, with a larger token increase. It does nothing with `between_tools`, because that setting means no up-front thinking. And treat `stop_reason: "max_tokens"` as a failed response, even if the JSON parses. No published numbers for this one. It's guidance: name the format positively, use tags for the parts you parse, and don't ask for the model's reasoning in the answer. On several current models, including Sonnet 5.5, that can trigger a `reasoning_extraction` refusal. Read summarized thinking blocks instead. The full post has what this teaser doesn't: **Read the full post:** [Four Prompt Patterns Worth Testing on Claude](https://blog.deveshk.dev/blog/claude-foundations-model-family-prompt-patterns-worth-testing-framework) *This is Day 6 of a series on Claude foundations. Day 5 covered XML tags for structuring prompts.*