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Agent Evaluation Scorecard Prompt
Grade agent output the same way every time — a rubric scoring correctness, grounding, safety, tone, and completeness, with a pass threshold instead of a gut call.
Structured Output
AI Output Validator
Paste an AI's output and check it against the structure you expected: real JSON parsing, heading and label checks, a 0–100 health score with a PASS / WARNING / FAIL verdict — and a repair prompt you send straight back to the model.
What output is being checked, and against what? E.g. "Validate support ticket classification output." Feeds the repair prompt's requirement line.
Paste the AI's response exactly as it came back — fences, prose, and all. That's what gets validated.
Validation Result
Repair Prompt — send this back to the model
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Agent Failure Analysis Prompt
Turn a failed case into a fix — diagnose where in the agent's flow it went wrong, categorize the failure, and point at the prompt, tool, or context that caused it.
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Catch Invalid AI Labels
The model answered "Complaints" — your set says "Complaint". One character of drift, one broken dashboard. Caught before it counts.
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Check AI Output for Missing Sections
The PRD looks done — but Non-Goals is gone and Requirements jumped above Goals. Section presence and order, checked in one pass.
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Conversation Quality Evaluation Prompt
Judge the whole conversation, not one reply — evaluate a multi-turn exchange for context retention, coherence, goal completion, and recovery from misunderstanding.
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Fix AI Format Drift
Run 40 looks nothing like run 1: sections reordered, one came back empty. Detect the drift, repair the run, keep the pipeline honest.
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Fix Invalid JSON from AI
The JSON won't parse and you can't see why. Deterministic cause-sniffing — trailing commas, single quotes, unclosed brackets — and the repair prompt that fixes it.
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Groundedness Check Prompt
Verify a RAG answer is actually from its retrieved context — every claim traced to a retrieved passage, and any answer that outran its sources flagged.
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Hallucination Detection Prompt
Catch the confident invention — check an AI output's claims against its source and flag every statement that isn't supported, with the unsupported span quoted.
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Repair AI Output with a Repair Prompt
Don't re-roll the whole response — send back a surgical prompt that fixes the violations and keeps everything that was right.
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Validate AI Output — Catch Format Violations
Paste the response, get the verdict: real JSON parsing, missing-field detection, and a repair prompt for everything found.
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Validate Classification Output
Label in the set? Case exact? Confidence in range? The checks that keep classification output usable for routing.
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Validate Markdown Structure
Heading scan against the skeleton: missing sections, broken order, absent title — the README that shipped without its Examples.
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Validate Structured Output from AI
Fields checked against the contract: missing ones flagged, invented ones caught, prose around the object detected.
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Validate Summary Output
Did the summary keep its contract? Section presence, preamble detection, and the "Here is the summary" tax — checked and repaired.
Workflow
AI Agent Evaluation Workflow
Find out whether an AI agent behaves before users do — define what correct means, build test scenarios with expected outputs, catch failures and hallucinations, then regression-test each version.
Workflow
AI Data Extraction Workflow
Turn messy text into structured data you can trust enough to feed another system — bound the source, extract the fields, force clean JSON, and validate before it flows downstream.
Workflow
AI Reliable JSON Output Workflow
Make any AI task return JSON your code can rely on — define the schema, force the model to it, validate every response, and diff the drift when a model update breaks the shape.
Workflow
AI Classification Workflow
Build a text classification step you can automate on — pull out the unit to classify, assign a label from a fixed set, and validate the label is one you actually allow.
Workflow
AI Programmatic SEO Workflow
Generate many intent-matched SEO pages from a template without the thin-content spam — map the search intents, define the page variables, build the template, and QA before publishing at scale.
Workflow
AI Data Pipeline & ETL Workflow
Design a pipeline that moves data without corrupting it — map the sources and ingestion, design the transformation stages, set validation and quality gates, then document the pipeline and monitoring.
Workflow
AI Content Writing Workflow
Turn source material and a target audience into clear docs, help, or knowledge-base content — gather the material, outline the piece, write it section by section, then check it for clarity and gaps.
Workflow
AI Code Documentation Workflow
Generate documentation that matches the code instead of drifting from it — have AI explain what the code really does, write it up as structured docs, then validate the format holds.
Project
Build an AI Research Assistant with AI
The full path to an AI research assistant — define its scope, organize the source corpus, ground responses in references, extract key facts, synthesize findings, check groundedness, then validate it for use.
Project
Build an AI Content Engine with AI
The full path to a content operation that runs, not a pile of posts — set the editorial strategy, research the topics, build a reusable template, then produce and QA structured pieces on repeat.
Project
Build a Programmatic SEO Site with AI
The full path to pages that rank at scale, not penalty bait — map the intents, build the data set, structure it, template the page, then QA before publishing hundreds.
Project
Build an AI Workflow Automation System with AI
The full path to automation that survives the real world — wire the integrations and triggers, design the control API, move the data through validated stages, evaluate the AI steps, then deploy.
Project
Build a Data Pipeline with AI
The full path to a pipeline that moves data without corrupting it — design the ingestion and transforms, extract and structure the sources, gate the quality, store it, then deliver and ship it monitored.
Guides for this tool
Guide
How to Make AI Return Valid JSON Every Time
AI models return broken JSON more often than you'd expect. Here's how to structure a prompt so the output parses cleanly, plus what to check before you trust it.
Guide
How to Stop AI From Inventing Missing Data
When a source is missing a field, AI tends to fill the gap with a plausible guess instead of saying it isn't there. Here's how to make the model mark missing data explicitly — and check the result before you trust it.
Guide
How to Keep AI Output Format Consistent
The AI nails your format once, then the next output adds a "Sure, here's…" wrapper, renames a field, turns the table into bullets, and drops a section. Here's how to write the format as a contract and catch the drift when it happens anyway.
Guide
How to Extract Data From Documents With AI Without Losing Evidence
AI pulls the fields you asked for, but hands back a flat list with no way to tell which values it read from the document and which it guessed. Here's how to make each extracted value carry its source quote, location, and a review flag — so you can check the result instead of trusting it.
How it works
State the validation goal, pick the expected output type — JSON, YAML, XML, CSV, markdown document, structured summary, classification, or extraction output — and list the expected structure, one item per line: field names for JSON, section headings for documents, allowed labels for classification. Then paste the AI's actual response, exactly as it came back, and click Validate Output. The engine runs a real check, not a string match: JSON gets parsed (with deterministic cause-sniffing when it doesn't), XML gets tag-stack well-formedness checking, CSV gets quote-aware column math, markdown gets a heading scan with order checking, classification gets label-set and confidence validation. You get a 0–100 health score with a PASS / WARNING / FAIL verdict, every issue listed with its fix — and a repair prompt you paste straight back into the model to get the corrected output. Nothing leaves your browser.
Best for
- Checking an AI's output against the format you expected
- Getting a repair prompt for every failure, not just a pass/fail
- Closing the loop after a structured-output prompt
Not for
- Building the prompt that produces the output — that is the JSON Output Prompt Builder
- Comparing two outputs for quality — that is the Prompt Comparator
Use cases
- Checking an automation's AI step output before it breaks the next node
- Diagnosing why a JSON response won't parse — fences, trailing commas, prose
- Verifying documents and summaries kept their required sections
- Catching invented labels before they pollute a classification pipeline
Pro tips
- Paste the response exactly as it came back — fences, preamble, and all. The wrapping is often the problem, and the validator detects it.
- The repair prompt is built to be surgical: "keep everything that was already correct unchanged, fix only the problems listed". Send it in the same conversation so the model still has its previous response in context.
- Use the structure list from your generator prompt: the fields you defined in the JSON Output Prompt Builder or the sections from the Markdown Output Builder are exactly what belongs in Expected Structure.
- A WARNING verdict is often one fence away from PASS — fence-wrapped but otherwise valid output is the most common failure and the cheapest repair.
FAQ
Does the output I paste in get sent anywhere, or is it checked in my browser?
It's checked in your browser — the AI response you paste and the expected-structure list you enter are analyzed locally with deterministic checks like JSON.parse and tag-stack matching, so nothing leaves your browser and no output is sent to a server. The check is real but not exhaustive; it flags structural problems, not whether the content is correct.
Is the validation real or just string matching?
Real, within what a browser can do deterministically: JSON goes through JSON.parse with cause-sniffing for trailing commas, single quotes, and unclosed brackets; XML gets tag-stack well-formedness checking; CSV gets quote-aware cell counting against the header; markdown and summaries get a heading scan with presence, order, and empty-section checks; classification output gets label-set membership, case, and confidence-format checks.
What does the health score mean?
A deterministic 0–100: every issue subtracts a weighted penalty — a parse failure costs far more than an unexpected field. 85+ is PASS, 50–84 WARNING, below 50 FAIL. The score is for triage, not for averaging: it tells you whether to ship the output, repair it, or regenerate from scratch.
What exactly is the repair prompt?
A ready-to-send prompt built from the issues found: it restates your original requirement, lists each problem with its specific fix ("include 'email'; use null if the value is unknown", "remove the ``` fences"), and instructs the model to return only the corrected output, changing nothing that was already right. Paste it into the same conversation and the model repairs its own response.
Can it validate YAML fully?
Structurally, yes; semantically, partially — there's no full YAML parser in the browser, so the validator checks the things that break consumers deterministically: forbidden tabs, multiple documents, code fences, and the presence of your expected top-level keys. For deep YAML schema validation, a dedicated parser in your pipeline is the right tool; this catches the format violations models actually make.
What if the output passes — do I still get a repair prompt?
No — a PASS with zero issues shows "no issues found" and skips the repair prompt entirely. The tool never invents problems to fix; an honest pass is the goal, not an opportunity for busywork.