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General

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What is QWED?

QWED is a deterministic verification protocol for Large Language Models (LLMs). It treats LLMs as “untrusted translators” and verifies their outputs using formal methods (SymPy, Z3, AST, SQLGlot).

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How is QWED different from other LLM tools?

FeatureQWEDRAGFine-tuningGuardrails
Deterministic✅ Yes❌ No❌ No❌ No
Mathematically Proven✅ Yes❌ No❌ No⚠️ Limited
No Training Required✅ Yes⚠️ Needs docs❌ No✅ Yes
Works Offline❌ API-based✅ Yes✅ Yes✅ Yes
QWED complements these tools - use RAG for knowledge, QWED for verification.

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Integration

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Do I need to run a backend server?

Yes! QWED requires a backend server with YOUR LLM API keys configured. Architecture: 
Your App → SDK → Backend Server (YOU run) → LLM (YOUR key) → Verifiers
Setup: 
# Step 1: Configure your LLM
cp .env.example .env
echo "ANTHROPIC_API_KEY=sk-ant-..." >> .env

# Step 2: Run backend
python -m qwed_api

# Step 3: Use SDK
python your_app.py
See Getting Started for full setup.

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Do I need to call my LLM first?

No! This is the most common mistake. ❌ Wrong: 
llm_result = openai.chat(...)  # Don't do this
qwed.verify(llm_result)  # Too late!
✅ Correct: 
# Let QWED backend handle LLM call
result = qwed.verify("your question")
The backend server (that YOU run) calls the LLM using YOUR API key.

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What LLM providers does QWED support?

You configure YOUR LLM provider in the backend’s .env file: Supported providers:
  • OpenAI (direct API)
  • Anthropic Claude
  • Azure OpenAI
  • AWS Bedrock
  • Google Gemini
Example .env: 
ACTIVE_PROVIDER=anthropic
ANTHROPIC_API_KEY=sk-ant-...
See LLM Configuration for all providers.

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Can I use my own LLM API key?

Yes! You MUST use your own LLM API key. QWED is open source - you run the backend server with YOUR credentials. You provide:
  • Your own LLM API key (in .env)
  • Your own backend server (run locally)
You control:
  • Which LLM provider to use
  • All your data and keys

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Costs

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How much does QWED cost?

Open Source: FREE! ✅
  • You just pay for your own LLM API usage
  • No QWED subscription needed
  • Run backend server yourself
Costs you pay:
  • Your LLM provider (OpenAI, Anthropic, etc)
  • Your hosting (if deploying backend)
Example: If using Anthropic Claude:
  • Input: $3 per million tokens
  • Output: $15 per million tokens
  • (See your LLM provider’s pricing)

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What are my rate limits?

QWED itself: No limits! It’s open source. Your LLM provider: Check their limits:
  • OpenAI: Tier-based (see dashboard)
  • Anthropic: Based on plan
  • Azure: Based on deployment
If you hit your LLM provider’s rate limit, implement retries: 
import time

try:
    result = qwed.verify(query)
except Exception as e:
    if "rate_limit" in str(e):
        time.sleep(60)  # Wait and retry
        result = qwed.verify(query)

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Security

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Is my data secure?

Yes. QWED:
  • Uses encrypted connections (HTTPS/TLS)
  • Doesn’t store verification queries by default
  • SOC 2 Type II compliant (Enterprise plan)
  • Supports on-premise deployment (Enterprise Pro+)

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Can QWED access my database/files?

No. QWED only sees:
  • The query you send
  • SQL schema (if verifying SQL)
  • Code snippet (if verifying code)
It cannot access your application database or files.

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How long is data retained?

Default: 30 days for audit logs
Enterprise: Configurable (90 days - 7 years)
On-premise: You control retention

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Performance

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How fast is QWED?

Average response times:
  • Simple queries: 1-2 seconds
  • Complex queries: 2-5 seconds
  • Batch processing: 0.5s per item
Factors affecting speed:
  • Query complexity
  • Network latency
  • Verification engine used

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Can I make QWED faster?

Yes:
  1. Use batch processing: 
    results = qwed.verify_batch(items)  # Faster than individual calls
  2. Cache results: 
    @cache
def cached_verify(query):
    return qwed.verify(query)
  3. Use async: 
    results = await qwed.verify_async(queries)

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Verification Domains

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What can QWED verify?

Supported domains:
  1. Math - Calculations, equations, algebra
  2. Logic - Propositional logic, SAT/UNSAT
  3. Code - Security vulnerabilities, syntax
  4. SQL - Injection attacks, tautologies
  5. Facts - Multi-source consensus
  6. Stats - Statistical claims
  7. Images - Visual verification
  8. Consensus - Multi-model agreement

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What can’t QWED verify?

Not supported:
  • Creative writing quality
  • Subjective opinions
  • Future predictions
  • Unstructured text summaries
Use cases: QWED is for objective, verifiable claims only.

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Errors & Debugging

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Why is my verification failing?

Common causes:
  1. Malformed input: 
    # ❌ Too vague
result = qwed.verify("calculate something")

# ✅ Specific
result = qwed.verify("Calculate 15% of 200")
  2. Wrong verification method: 
    # ❌ Wrong
result = qwed.verify(code_snippet)

# ✅ Correct
result = qwed.verify_code(code_snippet, language="python")
  3. Network issues: 
    result = qwed.verify(query, timeout=60)  # Increase timeout

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How do I debug verification failures?

Enable verbose mode: 
client = QWEDClient(api_key="...", verbose=True)
result = client.verify("2+2=4")
# Prints internal flow to console
Check trace: 
result = client.verify("2+2=4", return_trace=True)
print(result.trace)
# Shows LLM extraction → verification steps

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Production Deployment

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Is QWED production-ready?

Yes. QWED is used in production by:
  • Financial institutions (loan calculations)
  • Healthcare AI (drug interaction checking)
  • Legal tech (contract analysis)
  • EdTech (student assessment)

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How do I deploy QWED to production?

See Production Deployment Guide for full checklist. Quick steps:
  1. Test thoroughly in staging
  2. Deploy with feature flag
  3. Start with 5% traffic (canary)
  4. Monitor metrics
  5. Gradually increase to 100%

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What if QWED goes down?

Recommended:
  1. Implement fallback mechanism
  2. Cache recent results
  3. Graceful degradation
try:
    result = qwed.verify(query, timeout=5)
except QWEDError:
    # Fallback to cached/approximate result
    logger.warning("QWED unavailable, using fallback")
    return fallback_result()

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Support

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How do I get help?

Community (Free): Enterprise Support:
  • 📧 Email: support@qwedai.com
  • 💼 Slack Connect (Enterprise customers)
  • 📞 Emergency Hotline (Enterprise Pro+)
Response times:
  • Community: Best effort
  • Pro: 24-48 hours
  • Enterprise: 4-hour SLA
  • Enterprise Pro+: 1-hour SLA

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Can I request new features?

Yes! Submit feature requests: Most requested features:
  • Real-time streaming verification (Q2 2026)
  • Client-side verification (Q3 2026)
  • More language SDKs (ongoing)

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Still Have Questions?