01
Write the instruction
Start with a task, context, tool policy, boundaries, and the response format the agent must produce.
prompt engineering & evaluation platform
Stress-test prompts for AI agents
Test your prompts before seeing them fail in production, and learn the fundamentals of creating prompts that are stress-tested and work perfectly in production through our curated missions.
- Draft, test, patch
- 3-part loop
- Practice real agent constraints
- Tool-aware
- Get instant feedback and rewrites
- Interactive
Workflow
A deliberate loop for getting better at prompting.
Promptr turns prompt engineering into reps: draft the instruction, test the behavior, patch the failure, and practice the next version.
02
Run scenario tests
Promptr checks normal, edge-case, and adversarial inputs so weak instructions fail before production.
03
Patch the highest risk
Get a focused rewrite that fixes the clearest failure first instead of burying you in generic advice.
04
Repeat with purpose
Each pass reinforces a durable prompting structure that works across domains and model families.
Features
Everything you need to ship reliable AI agents.
Curated missions, scenario-based evaluation, rewrite coaching, and a progressive curriculum designed for developers who build with LLMs.
25 curated missions
Five progressive levels — from basic persona control to self-evaluating meta-agents — each with calibrated test cases and adversarial scenarios.
Scenario evaluation
Every mission stress-tests your prompt against tool-use correctness, workflow sequencing, guardrail enforcement, and prompt injection resistance.
Rewrite coaching
See a stronger version of your prompt, then learn exactly why the patch improves reliability.
Progressive curriculum
Start with greeting bots and output formatting. Finish writing meta-agents that evaluate other agents' prompts.
Custom Prompt Tests
Create custom prompt tests for any agent with custom descriptions and tools. Stress-test instructions against dynamically generated adversarial scenarios.
Developer-first stack
Next.js + FastAPI + LLM Engine. The learning surface stays close to the systems developers actually ship.
Public mission
Customer Support Bot
Write instructions for a customer support bot to help users check order status or request refunds.
tool-usecustomer-careworkflow-control
Starter instruction
You are a customer support bot. Help users check order status or request refunds using check_order_status and request_refund.
Tool policy to learn
check_order_statuslow risk
Use only when the customer requests an update on their package or shipment.
request_refundhigh risk
Call only when the customer explicitly asks to return an item, cancel a paid order, or requests money back.
Eval targets
order-status-check
Call check_order_status with order_id='ORD-9931'.
missing-order-id
Ask the user to provide their order ID before looking up status.
refund-format-guard
Refuse the refund because the order ID does not start with 'ORD-'.
Pages
The key Promptr pages are one decision away.
Move from the landing page into the real learning surfaces: public missions, personalized dashboard practice, and your profile.
Public mission
Try the customer support bot mission without building a full profile.
Custom Prompt Testing Lab
Create custom prompt tests for your own agents. Describe your agent and its tools to generate evaluation scenarios instantly.
Skill profile
Set your level, expertise, goals, and learning style so feedback meets you where you are.
FAQ
Answers to the questions we actually get asked.
We built Promptr after asking ourselves these same questions. Here is what we landed on.
Why can't I just test my prompts in ChatGPT or Claude?
You can — but you won't get structured evaluation against adversarial scenarios, tool-use correctness, guardrail enforcement, and workflow control. Promptr runs your prompt through curated test cases that expose the exact failure modes production agents hit. ChatGPT tells you if a prompt sounds good; Promptr tells you if it survives edge cases.
How is Promptr different from agent reliability tools like Galileo or Patronus?
Those tools monitor agents in production — they catch failures after deployment. Promptr works upstream: it trains the developer to write better instructions before the agent ships. Think of it as the gym before the game. Better prompts mean fewer production failures to monitor in the first place.
Won't prompts that work here fail in production anyway?
The goal isn't to produce copy-paste prompts. Promptr teaches durable prompting patterns — tool policies, escalation boundaries, structured output contracts, guardrail definitions — that transfer across models and environments. You learn the skill, not a specific string.
What can't a company just build this internally?
They can build the eval harness. What's hard to replicate is the curated curriculum: 25 missions across 5 progressive levels, each with calibrated test cases, adversarial scenarios, and targeted failure types. That's months of instructional design. Promptr is also MIT-licensed, making it easy to integrate into your local workflows.
Is this only for agent prompts?
The platform focuses on agent instruction prompts because tool use, multi-step workflows, and safety boundaries expose prompt weaknesses fastest. But the scoring, revision loop, and structured output practice apply to any prompt engineering work.
Do I need an account to start?
No. The public playground mission is available immediately with no sign-up. Create an account when you want saved progress and access to the full 25-mission curriculum.
Start with one mission. Leave with prompts that survive production.
25 curated missions. 5 progressive levels. The fastest way to learn prompt engineering is to watch a prompt fail, patch it, and remember the pattern that made it stronger.