Where the prompt generator starts from a blank page, the Console's prompt improver starts from a prompt you already have and rebuilds it for rigor. Anthropic's docs say it "excels at making prompts more robust for complex tasks that require high accuracy" — and, unusually for product documentation, they also spell out the cost. Both halves deserve equal attention.
The four documented stages
The improver works through four steps, per Anthropic's docs:
1. Example identification. It locates the examples already present in your prompt template — the sample inputs and outputs that show Claude what success looks like — so they survive the rewrite rather than being flattened into prose.
2. Initial draft. It produces a restructured template with clearer section organization. In practice this leans on XML-style tags to separate instructions, context, and data — a structure that reduces the classic failure where Claude confuses your instructions with the document it's processing.
3. Chain-of-thought refinement. It adds and polishes explicit reasoning instructions — directing Claude to think through the problem step by step before answering. This is the single biggest lever for accuracy on complex tasks, and the single biggest contributor to the latency cost discussed below, because reasoning is generated as output tokens before the answer starts.
4. Example enhancement. Existing examples are upgraded to match the new structure, including demonstrating the reasoning process, and standardized in format.
The docs list the characteristic output features: chain-of-thought instructions, XML tag organization, standardized example formatting, and "strategic prefills that guide Claude's initial responses."
The documented trade-off: longer, slower, more expensive
Anthropic's docs state it directly: "For latency or cost-sensitive applications, consider using simpler prompts. The prompt improver creates templates that produce longer, more thorough, but slower responses." The mechanics are straightforward. The improved template itself is longer, so every request carries more input tokens. Chain-of-thought instructions make Claude generate reasoning before the answer, so every response carries more output tokens — which cost more per token than input on every Claude model, and which stream out in real time, so the user waits through the thinking. The result is a genuine three-way trade of the kind covered in multidimensional evaluation: accuracy up, latency up, cost up.
| Application profile | Improver verdict |
|---|---|
| Complex analysis, high error cost, async or batch flow | Strong fit — accuracy gains are cheap when nobody is waiting |
| Interactive chat with a human watching the cursor | Test carefully — added reasoning delay is directly visible |
| High-volume, cost-ceilinged tasks (classification, routing) | Often a poor fit — a simpler prompt on a fast model may beat it on the metrics that matter |
Measure the trade, don't guess it
The improver hands you two competing prompt versions — which is precisely the situation the Console's Evaluation tool exists for. Run the original and the improved template against the same test suite, compare outputs side by side, grade quality on the 5-point scale, and record response length. If the improved prompt's accuracy gain shows up in your grades and your latency budget survives the longer outputs, ship it. If quality was already at ceiling, the docs' advice applies: keep the simpler prompt — and revisit the improver only when your test suite surfaces accuracy problems that structure and reasoning could plausibly fix.
Where to go next
The full loop is generator → improver → evaluation: see the Evaluation tool walkthrough and the Console toolchain as a system. For why prefills are being phased out on newer models, see the prefill deprecation explainer.