# Why Cloud LLMs for QWED Verification?

**TL;DR:** Verification is a critical task requiring maximum accuracy. Cloud LLMs (GPT-4, Claude) deliver 90-95% accuracy vs 70-80% for local models, making them ideal for production verification.

---

## 🎯 The Core Problem

**Verification requires two things:**
1. **LLM understands the query** (natural language → structured reasoning)
2. **Symbolic verifier proves the answer** (SymPy, Z3, AST)

If the LLM gets step 1 wrong, verification fails even with perfect symbolic math.

---

## 📊 LLM Accuracy Comparison

### Math Verification Example

**Query:** "What is the integral of 2x?"

| Model | Type | Accuracy | Typical Response |
|-------|------|----------|------------------|
| **GPT-4o-mini** | Cloud | ~95% | "x² + C" ✅ |
| **Claude 3 Haiku** | Cloud | ~93% | "x² + C" ✅ |
| **Llama 3 8B** | Local | ~75% | Sometimes "x² + C" ✅, sometimes "2x²/2" ❌ |
| **Mistral 7B** | Local | ~70% | Inconsistent, may confuse derivative/integral |

### Why This Matters

**When QWED verifies:**
```
1. LLM says: "x² + C"
2. SymPy computes: integrate(2*x, x) = x**2
3. QWED compares: ✅ MATCH!
```

**If LLM is wrong:**
```
1. LLM says: "2x²" (incorrect)
2. SymPy computes: x**2
3. QWED: ❌ NO MATCH → Verification fails
```

**Result:** User sees failure, even though QWED's symbolic engine is correct!

---

## 🤔 When to Use Each

| Use Case | Local LLM (Ollama) | Cloud LLM (OpenAI/Anthropic) |
|----------|-------------------|------------------------------|
| **Development/Testing** | ✅ Free, fast iteration | ⚠️ Costs add up |
| **Production (Critical)** | ❌ Lower accuracy | ✅ **Recommended** |
| **Privacy-Sensitive Data** | ✅ 100% local + PII masking | ⚠️ Use with PII masking |
| **Cost-Sensitive** | ✅ $0/month | ⚠️ ~$5-50/month |
| **High-Stakes Decisions** | ❌ Risk of errors | ✅ **Recommended** |

---

## 💡 QWED's Hybrid Approach

**Best Practice: Use both strategically**

### Development Setup (Free)
```python
from qwed_sdk import QWEDLocal

# Local LLM for development
client_dev = QWEDLocal(
    base_url="http://localhost:11434/v1",  # Ollama
    model="llama3",
    cache=True  # Cache responses
)

# Test your queries
result = client_dev.verify("What is 2+2?")
```

**Cost:** $0/month  
**Use for:** Prototyping, experimentation, learning

### Production Setup (Reliable)
```python
import os
from qwed_sdk import QWEDLocal

# Cloud LLM for production
client_prod = QWEDLocal(
    provider="openai",
    api_key=os.getenv("OPENAI_API_KEY"),
    model="gpt-4o-mini",
    mask_pii=True,   # Privacy protection
    cache=True        # 50-80% cost savings!
)

# Critical verification
result = client_prod.verify("Verify calculation: ...")
```

**Cost:** ~$5-10/month (with caching!)  
**Use for:** Production, high-stakes decisions

---

## 💰 Cost Analysis

### Local LLM (Ollama)
- **Setup:** 10 minutes (download model)
- **Monthly Cost:** $0
- **Accuracy:** 70-80% on math/logic
- **Privacy:** 100% local
- **Best for:** Development, testing, learning

### Cloud LLM (OpenAI GPT-4o-mini)
- **Setup:** 2 minutes (API key)
- **Monthly Cost:** $5-10 (with caching)
- **Accuracy:** 90-95% on math/logic
- **Privacy:** Use PII masking
- **Best for:** Production, critical tasks

### With QWED Caching (Smart Cost Savings)
```python
# First query: Hits LLM (costs $$)
result1 = client.verify("What is 2+2?")

# Same query within 24 hours: Cache hit (FREE!)
result2 = client.verify("What is 2+2?")  # $0 cost!
```

**Real Savings:** 50-80% cost reduction on repeated queries!

---

## 🔒 Privacy Considerations

### Local LLM Advantages
✅ **100% private** - data never leaves your machine  
✅ **No API keys** - no third-party access  
✅ **Compliance** - easier GDPR/HIPAA compliance

### Cloud LLM with PII Masking
```python
client = QWEDLocal(
    provider="openai",
    mask_pii=True,  # Auto-mask emails, SSNs, etc.
    pii_entities=["EMAIL_ADDRESS", "CREDIT_CARD", "US_SSN"]
)

# Sensitive data protected!
result = client.verify("User email: john@example.com, calculate 2+2")
# OpenAI sees: "User email: <EMAIL_ADDRESS>, calculate 2+2"
```

**Result:** Cloud accuracy + local privacy! 🔒

---

## 🎯 Recommendation by Use Case

### Healthcare (HIPAA)
```python
# Option 1: Local LLM (most private)
client = QWEDLocal(
    base_url="http://localhost:11434/v1",
    model="llama3"
)

# Option 2: Cloud + PII masking (more accurate)
client = QWEDLocal(
    provider="openai",
    mask_pii=True,
    pii_entities=["PERSON", "US_SSN", "MEDICAL_LICENSE"]
)
```

**Recommendation:** Cloud + PII masking for critical diagnoses

### Finance (PCI-DSS)
```python
client = QWEDLocal(
    provider="openai",
    mask_pii=True,
    pii_entities=["CREDIT_CARD", "IBAN_CODE"]
)
```

**Recommendation:** Cloud + PII masking (accuracy matters for money!)

### Enterprise (General)
```python
# Development
dev_client = QWEDLocal(base_url="http://localhost:11434/v1", model="llama3")

# Production
prod_client = QWEDLocal(provider="openai", mask_pii=True, cache=True)
```

**Recommendation:** Hybrid approach

---

## 🚀 The QWED Advantage

**Even with local LLMs, QWED catches errors!**

### Scenario: Local LLM Makes Mistake
```python
client = QWEDLocal(base_url="http://localhost:11434/v1", model="llama3")

# Llama 3 might say: "Derivative of x² is x" (WRONG!)
result = client.verify("What is the derivative of x²?")

# QWED's symbolic verification:
# SymPy: diff(x**2, x) = 2*x
# LLM said: "x"
# QWED: ❌ NO MATCH! 
# result.verified = False
```

**User sees:** "Verification failed - LLM answer doesn't match symbolic proof"

**But:**
- More failures = worse UX
- Cloud LLMs = fewer verification failures = better UX

---

## 📈 Accuracy in Practice

**From QWED internal testing:**

| Domain | Local LLM (Llama 3 8B) | Cloud LLM (GPT-4o-mini) |
|--------|----------------------|-------------------------|
| Basic Math | 85% | 98% |
| Calculus | 75% | 95% |
| Logic (SAT) | 70% | 93% |
| Code Security | 80% | 96% |

**Takeaway:** Cloud LLMs reduce verification failures by 15-25%!

---

## 🎓 Bottom Line

### Start with Local LLM
```bash
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Download model
ollama pull llama3

# Use with QWED
python -c "from qwed_sdk import QWEDLocal; \
  client = QWEDLocal(base_url='http://localhost:11434/v1', model='llama3'); \
  print(client.verify('2+2'))"
```

**Perfect for:** Learning, prototyping, hobby projects

### Scale to Cloud LLM
```bash
# Get API key from OpenAI
export OPENAI_API_KEY="sk-..."

# Use with QWED
python -c "from qwed_sdk import QWEDLocal; \
  client = QWEDLocal(provider='openai', mask_pii=True, cache=True); \
  print(client.verify('2+2'))"
```

**Perfect for:** Production, enterprise, critical decisions

---

## 🔗 Related Documentation

- **[LLM Configuration Guide](LLM_CONFIGURATION.md)** - Complete LLM setup
- **[PII Masking Guide](PII_MASKING.md)** - Privacy protection
- **[Caching Guide](QWED_LOCAL.md#caching)** - Cost savings

---

## ❓ FAQ

**Q: Can I use Llama 3 70B instead of GPT-4?**  
A: Yes! Larger local models (70B+) approach cloud accuracy but require significant hardware (40GB+ VRAM).

**Q: Is Ollama really free?**  
A: Yes! Fully open source. You just need hardware to run it.

**Q: What about Google Gemini?**  
A: QWED supports Gemini! Similar accuracy to GPT-4/Claude.

**Q: Can I switch between local and cloud?**  
A: Absolutely! Change the `provider` parameter anytime.

**Q: Do I need PII masking with local LLMs?**  
A: Not necessarily, but it's still good practice for audit trails.

---

**The choice is yours - QWED works with both!** 🚀

**Recommendation:** Start local (free), scale to cloud (reliable) when it matters.
