Try Grounded AI
User Guide

Try Grounded AI is an AI hallucination testing platform built by KiwiQA. It lets you paste any AI-generated response and run 7 independent validation checks — returning a structured verdict, a GR reliability score, and specific recommended actions in under 60 seconds.

Unlike developer-focused tools that require SDK integration and engineering setup, Try Grounded AI works for QA testers, product managers, consultants, and compliance teams with no code required.

Who is it for?

What problems does it solve?

• AI models hallucinate facts, citations, dates, and figures — even in production.
• Traditional test suites check for software bugs, not factual accuracy.
• Manual review of AI responses is inconsistent and doesn't scale.
• Regulated industries (healthcare, legal, finance) face real liability from hallucinated AI outputs.

Your first hallucination test takes under 60 seconds. No setup. No SDK. No integrations required.

Every test returns a GR (Grounded Reliability) rating — a score from 0 to 100 mapped to five bands. GR is the primary output of Try Grounded AI and the metric your team should use to gate AI deployments.

How the GR score is calculated

The GR score is a weighted composite of all up to 9 validation layers. Each layer contributes a percentage of the total score:

High-risk domain multiplier

For responses in healthcare, legal, finance, or government contexts, a 0.92× multiplier is applied to the final score. This raises the bar for these industries where hallucinations carry real-world liability.

Every test runs up to 9 independent checks. Each layer is designed to catch a different type of hallucination or reliability failure.

STRUCTURED DATA FIDELITY — 9TH LAYER

The 9th layer lets you attach any CRM export, CSV, or JSON dataset to your Response Audit. Grounded automatically parses every field, detects data types, computes aggregates, and verifies every AI claim against your actual data — no manual field mapping required.

HOW TO USE IT

1. Open Response Audit and fill in Steps 01 and 02.
2. Scroll to Step 04 — Attach CRM or structured data and click Enable.
3. Upload a CSV or JSON file, or paste the data directly. Max 500KB.
4. Run the audit. The Data Attribution tab will show a field-by-field verification with MATCHED / MISMATCH / INFERRED status for every claim.

SUPPORTED FORMATS

HOW GROUNDED VERIFIES DATA

Grounded uses a three-tier deterministic engine — no LLM arithmetic:

1. Type detection — every field is auto-classified as boolean, numeric, date, enum, or ID based on its values.
2. Aggregate computation — sums, averages, counts, and group-bys are computed in code before any LLM call. Results are exact.
3. Claim verification — Claude receives the computed ground truth and judges whether the AI's claims match it.

Example: if you ask "What is the average days open for SLA breached tickets?" and your CSV has 20 ticket records, Grounded computes the exact average (e.g. 14.5) and verifies the AI's answer against that — not against Claude's own recollection.

Use Single Test to validate one AI response at a time. This is the most common workflow for spot-checks, pre-release validation, and testing during development.

Test Suite mode

Test Suite lets you build a named set of test cases and run them as a group. Use this for sprint regression testing or pre-release checklists.

1. Switch to Test Suite tab within New Test.
2. Name your suite (e.g. "Sprint 14 — AI Chatbot").
3. Add individual test cases with question, response, and optional reference.
4. Click Run Suite. Each case runs through the full 9-layer pipeline.
5. View a suite summary report with aggregate GR score and per-case breakdown.

Batch Testing lets you upload a file of AI responses and test all of them in one operation. Use this for regression testing after model updates, auditing large content sets, or testing a full FAQ or knowledge base.

Supported file formats

CSV example

question,aiResponse,refDoc
"What is the SG rate for 2024-25?","The rate is 11%",""
"What does TGA stand for?","Therapeutic Goods Administration",""

Running a batch

Custom Rule Sets are the most powerful differentiation in Try Grounded AI. They let you upload your own verified facts — things only your organisation knows — and have them checked automatically on every test.

What to put in Custom Rules

• Founding year, headquarters, company name variations
• Product pricing, plan limits, feature availability
• Regulatory references specific to your jurisdiction
• Internal policy figures (thresholds, limits, rates)
• Correct names for people, products, and services

File format

CSV example

claim,correct_answer,source
"KiwiQA founded 2010","Founded in 2020","kiwiqa.ai/about"
"NZ-based","Headquartered in Sydney","kiwiqa.ai/about"
"manual testing only","Manual + automated QA","kiwiqa.ai/services"

Adding rules manually

You can also add individual rules without a file by using the manual entry form in the Custom Rules tab. Enter the claim, correct answer, and source URL, then click Add.

Test History

All tests — single, suite, and batch — are automatically saved to Test History. Use it to track results over time, re-run previous tests, and export reports.

• Search: Filter by question text or batch name.
• Filter: Show only PASS, WARN, FAIL, or batch results.
• Pagination: 20 results per page for easy navigation.
• Export CSV: Download your full history as a spreadsheet.
• Re-run: Click ↺ Re-run on any row to load it back into the test form.

Risk Profile

The Risk Profile dashboard gives you an aggregate view of your AI reliability over time. It's designed for team leads, QA managers, and compliance officers who need a summary view.

• Trend chart: GR score over time with selectable date ranges.
• Layer breakdown: Which of the 7 layers is failing most often.
• Regression alerts: Automatic flag when your average GR score drops.
• Period comparison: Compare this week vs last week.
• Export PDF: Download a formatted Risk Profile report.

The Team feature lets you invite colleagues to share your workspace. All team members share the same run quota, test history, and custom rules — there's no per-seat pricing.

Inviting a colleague

Managing team members

• Re-send: Generate a fresh invite link for a PENDING member.
• Remove: Revoke a member's access to your workspace.
• Member count: Shown as a badge on the Team nav item.

The Try Grounded AI API lets you run hallucination checks programmatically — from CI/CD pipelines, automated test suites, or your own applications. Available on Team plan and above.

Authentication

Generate an API key in the API Keys tab of the dashboard. Keys use the grnd_ prefix. Pass your key as a Bearer token in the Authorization header.

Run a test via API

curl -X POST https://grounded-topaz.vercel.app/api/v1/detect \
  -H "Authorization: Bearer grnd_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "question": "What is the SG rate for 2024-25?",
    "aiResponse": "The superannuation guarantee rate is 11%.",
    "refDoc": ""
  }'

Response format

{
  "score": 42,
  "grRating": "GR-2",
  "risk": "HIGH",
  "verdict": "FAIL",
  "finding": "Incorrect SG rate — domain rule violation detected.",
  "stages": {
    "consistency": { "score": 38 },
    "grounding":   { "score": 45 },
    "confidence":  { "score": 61 },
    "consensus":   { "score": 33 },
    "drift":       { "score": 80 },
    "deterministic": { "score": 12, "findings": ["SG rate mismatch"] }
  }
}

GitHub Actions example

- name: Run hallucination check
  run: |
    RESULT=$(curl -s -X POST \
      -H "Authorization: Bearer ${{ secrets.GROUNDED_API_KEY }}" \
      -H "Content-Type: application/json" \
      -d '{}'  \
      https://grounded-topaz.vercel.app/api/v1/detect)

    SCORE=$(echo $RESULT | jq '.score')
    if [ "$SCORE" -lt 60 ]; then
      echo "GR score $SCORE is below threshold. Blocking deployment."
      exit 1
    fi

The InsightReport is the full output of a single test. Here's how to read each section.

The GR score

The top-level score (0–100) and GR rating (GR-1 to GR-5) is your primary signal. Use this to decide whether the AI response is safe to use:

Per-layer scores

Each of the 7 layers returns an individual score. A score below 60 on any layer is a warning signal even if the overall GR rating is acceptable.

The Recommended Action

Every test includes a plain-English recommended action explaining what was found and what to do about it. This is designed to be readable by non-engineers and can be included directly in defect reports.

Exporting reports

• PDF export: Timestamped, formatted report suitable for client delivery or compliance documentation.
• Re-run: Load a previous test back into the form to test a revised AI response.
• CSV export: From Test History, export all results as a spreadsheet for analysis.