The Claude Console — Anthropic's web workbench — includes a prompt generator, a prompt improver, and an Evaluation tool. Teams often discover them one at a time and use them in isolation. That misses the point. Anthropic's documentation describes the three as a chain: the generator decides what variables a prompt needs, the improver maintains them, and the Evaluation tool tests, scales, and tracks versions by separating the variable portions of a prompt from the fixed portions. The connective tissue is the {{double_bracket}} template syntax.
The variable contract
A prompt template splits your prompt into two parts: fixed instructions that stay the same on every call, and variables — marked with double brackets like {{customer_email}} — that change per request. Anthropic's docs list the benefits as consistency, efficiency, testability, scalability, and version control. One practical note: the claude.ai chat product does not support prompt templates or variables — this is a Console and API concept.
The contract matters because the Evaluation tool literally requires it. Per the docs, your prompt must include "at least 1-2 dynamic variables using the double brace syntax: {{variable}}" before you can build an eval test set. A prompt written as one hard-coded block cannot be evaluated at scale, because there is nothing to substitute test cases into.
Step one: the generator decides the variables
The prompt generator takes a plain-language task description and "guides Claude to create high-quality prompt templates tailored to your specific tasks." Anthropic pitches it as a cure for the blank-page problem, and it is compatible with all Claude models, including those with extended thinking. Crucially, the generator's output arrives already structured as a template — it decides which parts of your task should become {{variables}}. That early decision defines the schema every downstream test case will fill in.
If you want to see how it works, the underlying metaprompt is public: Anthropic publishes a prompt generator Google Colab notebook (it requires an API key to run).
Step two: the improver preserves them
The prompt improver rewrites an existing prompt to be "more robust for complex tasks that require high accuracy," working in four documented steps: example identification, an initial draft, chain-of-thought refinement, and example enhancement. Its output adds chain-of-thought instructions, XML-tag organization, and standardized example formatting — while keeping your variables intact, so the improved prompt still honors the same contract the generator established.
Two caveats from the docs are worth flagging. First, the improver creates "templates that produce longer, more thorough, but slower responses" — for latency- or cost-sensitive applications, simpler prompts may serve you better. Second, improver output can include "strategic prefills that guide Claude's initial responses"; prefilling the assistant turn is not supported on current-generation models such as Claude Fable 5 and Opus 4.8, so review improved prompts before deploying them there (see the prefill deprecation article).
Step three: the Evaluation tool tests across the contract
The Evaluation tool lives in the 'Evaluate' tab of the Console prompt editor. Because your template's variables define the columns of a test set, you can populate rows three ways: manually via '+ Add Row', automatically via 'Generate Test Case' (Claude generates one row per click, and the generation logic is editable via 'Show generation logic' for greater precision), or by importing a CSV.
From there the loop closes. If you update the prompt text, you can re-run the entire suite against the new version to see how the change affects every test case. Results support side-by-side comparison of two or more prompts, quality grading on a 5-point scale, and prompt versioning. As a point of interest, the eval-tool documentation notes the Console's built-in prompt generator is powered by Claude Sonnet 4.5.
Where to go next
For a hands-on walk through the eval tab, see the Evaluation tool walkthrough; for the improver's trade-offs in depth, see prompt improver trade-offs. Broader template design guidance lives in prompt template design.