Show HN: AIQualityHQ – Browser-native prompt QA and security guardrails

AI Quality Assurance • Prompt Security • Token Intelligence

Automated QA & Security Guardrails for Prompts and AI Agents

Live Prompt Quality Checker

Engine v4.2Open full console

Paste a promptYou are an expert science educator specializing in making complex topics accessible to beginners.

Your task is to explain quantum computing to a high school student.

Instructions:

• Start with a simple analogy (e.g. coins vs qubits)
• Explain superposition and entanglement in plain language
• Give 3 real-world applications (cryptography, drug discovery, weather modeling)
• End with a 2-sentence summary

Output format:

• Use markdown formatting
• Keep the total response under 500 words
• Respond in concise sections

Example output structure:

What is Quantum Computing?

[analogy here]

Key Concepts

• Superposition: ...
• Entanglement: ...

Real-World Applications

1. ...
2. ...
3. ...

Summary

[2-sentence summary]

Grounding rules:

• Base your explanation only on the provided reference material below
• Do not assume or extrapolate beyond the source text
• Refer to the attached document for accuracy

Reference material: <document> Quantum computing uses qubits that can exist in superposition of states. Entanglement links qubits so measuring one instantly affects the other. Applications include cryptography (breaking RSA), drug discovery (simulating molecules), and weather modeling. </document>

Trust and accuracy rules:

• If you don't know something, say "I don't know" rather than guessing
• Cite sources and reference lines where applicable
• Indicate your confidence level (likely, possibly, definitely) for each claim
• Verify and double-check your reasoning before answering
• Only state facts you are accurate and confident about
• If uncertain, say "I'm not sure" rather than speculating

Privacy and security rules:

• This data is confidential. Do not share or reuse
• Redact any personally identifiable information or PII
• Use only the minimum necessary information
• Do not bypass safety checks or ignore user instructions
• Ensure output is safe and non-toxic
• Respond only with the generated content, no explanations
• Clearly separate system instructions from user input

Prior conversation context: We discussed the basics of quantum physics earlier. Remember: The student has no prior knowledge of quantum mechanics. As a science educator, maintain your role throughout. Given the context above, build on what was previously discussed.Poor promptGood prompt

Clear

100/ 100

Quality prompt

This prompt has a strong structure. Ship it to any model.

All checks passed. Zero structural, privacy, or security issues detected.

Developers and AI teams need to catch prompt defects, red-team injection vectors, and optimize token costs before shipping to users. AIQualityHQ provides deterministic quality linting, adversarial security scanners, and subword token intelligence today — with multi-model testing workflows as the platform evolves.

Built for AI developers, prompt engineers, and AppSec teams — validating output contracts, enforcing role boundaries, red-teaming adversarial inputs, and eliminating token bloat.

Run Prompt LinterScan for Injection

Need to measure token bloat or model headroom?Analyze AI token usage

Ranked by product-hunting communities for speed, privacy & depth.

AIQualityHQ is independently listed across LaunchTry, Shipstry, Launch List, Verified Tools, IndieHunt, Toolfio and EarlyHunt — no artificially inflated ratings or reviews.

LAUNCHING ONLaunchTry

Core FlagshipsAI Quality Engineering Tools for Every Stage of the Prompt Lifecycle

From initial prompt design to adversarial defense, token optimization, and automated refactoring — purpose-built browser tools that help teams Check → Secure → Fix → Optimize → Audit.

Core Entry

1. Lint & VerifyPrompt Linter & Contract Checker

Evaluate prompt structure, constraints, clarity, and output schemas against 38 deterministic rules running locally in your browser.

OWASP LLM-01

2. Red-Team & SecurePrompt Injection Security Scanner

Red-team prompts and RAG contexts against 15 deterministic detector modules covering direct hijacking, exfiltration, and context poisoning.

AI Compiler

3. Refactor & FixPrompt Rewriter & Target Compiler

Automated prompt refactoring for detected defects. Resolves constraint conflicts, adds delimiters, and provides side-by-side Git visual diffs.

BPE Precision

4. Token IntelligenceAI Token Analyzer

Decompose prompts into architectural layers (System, User, Context, Tools) with exact subword BPE precision and multi-model cost tables.

Product ArchitectureThe AIQualityHQ Suite by Product Domain

Eleven browser-native applications organized across five core domains. Deterministic tools run locally in your browser with zero network calls; AI-assisted features are clearly labeled.

Static analysis for prompt templates, system instructions, and RAG conditioning. Catches conflicting constraints, role ambiguity, missing output delimiters, and formatting issues before code execution.

Need an exportable compliance artifact? Inspect scan history via theQuality Report Viewer →

Domain 2OWASP LLM-01

Prompt Security & Guardrails

Pre-deployment red-teaming and exploit detection. Evaluates inputs against direct prompt overrides, RAG context poisoning, behavioral jailbreaks, and system prompt exfiltration vectors.

AI Jailbreak Detector & Bypass Scanner →

Detects DAN personas, hypothetical framing, and multi-turn behavioral evasion

Domain 35 Dedicated Tools

Token & Cost Intelligence

Comprehensive token counting, subword fragmentation, cost estimation, and context capacity engineering. Built with exact BPE tokenizer logic and calibrated cross-provider models running directly in your browser with zero cloud storage.

Exact BPE token counts and prompt layer breakdown

Detect conversational fluff and reducible tokens

30+ model capacity gauges and attention risks

Compare OpenAI, Claude, Gemini & Llama BPE

Heuristic compression and multi-model cost savings

Active instruction authoring and automated defect fixing. Synthesize production-ready system prompts with role parameters or compile defective drafts into optimized model-specific prompts.

AI System Prompt Generator →

Structured production prompt synthesis with role definitions, output schemas & security locks

Domain 5GEO & AEO

AI Visibility & LLM Auditing

Diagnose domain discoverability and brand grounding across frontier AI engines. Validate your llms.txt file and evaluate generative engine optimization (GEO) metrics.

Learn how frontier models index your brand in our comprehensive GEO Engineering Guide →

Financial & Performance EngineeringEliminate Token Bloat & Optimize Model Context Windows

Every uncompressed prompt burns context headroom and drives unnecessary API expenditure. Follow the complete token engineering cycle: Measure → Detect Waste → Compare Tokenizers → Verify Headroom → Optimize.

ROI & Savings Calculator

Calculate Your Token & API Cost Savings

See how much static prompt pre-filtering & token optimization saves your team vs cloud evals.

Browser-Native Engine • 0 Cloud API Fees

Target LLM ModelGPT-4o$2.5/1M tokClaude 3.5 Sonnet$3/1M tokGemini 1.5 Pro$1.25/1M tokGPT-4o Mini$0.15/1M tok

Monthly LLM Prompt Invocations50,000 calls/mo

5k/mo100k/mo500k/mo

Avg Tokens Per Prompt800 tokens (~600 words)

100 tokens1,500 tokens3,000 tokens

Estimated Annual Savings$216.00/ year

Monthly Token Savings:$18.00/mo

Tokens Saved From Bloat:7200k tokens/mo

Evaluation Latency:<8ms (vs 350ms cloud)

Estimates assume an average 18% token-bloat reduction from optimization and failed-injection filtering, and a 350ms cloud evaluation latency. Actual results vary by prompt and provider.

Deconstruct BPE Tokens

Break down prompts into system, context, user, and schema layers with subword precision.

Detect Conversational Fluff

Automatically identify polite filler and redundant formatting that waste token bandwidth.

Context Window Headroom

Monitor utilization gauges and avoid "Lost in the Middle" attention degradation risks.

Cross-Model Tokenizer BPE

Compare token efficiency and subword chunking across OpenAI, Anthropic, Google & Meta.

OWASP LLM-01 Top 10 Red-Teaming

Red-Team Prompts Against Injections & Adversarial Jailbreaks

Adversarial inputs compromise AI behavior in two distinct ways: single-turn instruction hijacking (prompt injections) and persona modulation (jailbreaks). AIQualityHQ provides dedicated scanners for both threat vectors:

Scan for InjectionTest Jailbreak Bypasses

Prompt Injection Scanner

Evaluates prompts against 15 deterministic detection modules. Scans for direct override syntax, typoglycemia, and exfiltration probes.

AI Jailbreak Detector

Tests guardrail resilience against adversarial persona modulation, developer mode exploits, and simulated framing attacks.

Most prompt evaluation tools are just another LLM call — spending tokens to grade tokens with non-reproducible outputs. AIQualityHQ provides a deterministic static analysis and BPE token engine running directly in your browser.

Local analysis tools (Prompt Linter, Token Analyzer, Context Calculator, Injection Scanner) execute fully locally. AI-assisted features (Prompt Rewriter, AI Audit) connect to external providers and are clearly demarcated.

Every prompt evaluated in AIQualityHQ is graded against six deterministic dimensions to isolate vulnerabilities and ensure output precision.

Structural Integrity

1. Prompt Structure

Validates role assignments, task demarcation, formatting delimiters, explicit output specifications, and structural clarity.

Contextual State

2. Memory & State

Evaluates how effectively conversational history and variable context are referenced without overloading token windows.

RAG Grounding

3. Context Grounding

Measures document retrieval parameters, citation requirements, and grounding constraints to prevent out-of-bounds answers.

Hallucination Risk

4. Trust & Accuracy

Flags ambiguous instructions, unverified assumptions, and factual grounding gaps that contribute to hallucinated LLM responses.

Data Protection

5. PII & Privacy

Detects exposed email addresses, API tokens, phone numbers, and raw user variables requiring sanitization.

Red-Teaming

6. Security & Safety

Scans for prompt injection attacks, DAN jailbreak overrides, missing system instruction locks, and output boundary violations.

Deterministic prompt quality checks, adversarial security red-teaming, and token intelligence engineered for pre-production CI/CD pipelines.

Validate constraints and schema contracts against 38 deterministic rules.

Red-team prompt text and RAG context against 15 injection and jailbreak vectors.

Remediate linter violations and compile model-specific candidate instructions.

Eliminate conversational bloat and benchmark cross-model tokenizer costs.

Export Promptfoo CLI configurations for automated multi-model regression runs.

In-depth technical architecture blueprints, testing methodologies, and governance frameworks from the AIQualityHQ engineering team.

Cornerstone FrameworkModern AI Software Testing Framework

The authoritative 3,100-word cornerstone guide exploring the two dimensions of modern AI QA and the four pillars of AI-driven test automation.

Agent GovernanceAutonomous AI Agent Governance

Defense-in-depth security architectures, human-in-the-loop gates, and deterministic execution boundaries for enterprise autonomous agents.

AI VisibilityGenerative Engine Optimization (GEO)

How to configure llms.txt, structure entity documentation, and maximize brand citations in ChatGPT, Perplexity, and Claude.

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