# 🔄 GPT-OSS Advanced Critique & Improvement Loop

A Streamlit app demonstrating the "Automatic Critique + Improvement Loop" pattern using GPT-OSS via Groq.

## 🎯 What It Does

This demo implements an iterative quality improvement process:

1. **Generate Initial Answer** - Uses Pro Mode (parallel candidates + synthesis)
2. **Critique Phase** - AI critic identifies flaws, missing information, unclear explanations
3. **Revision Phase** - AI revises the answer addressing all critiques
4. **Repeat** - Continue for 1-3 iterations for maximum quality

## 🚀 Key Features

- **Iterative Improvement** - Each round makes the answer better
- **Transparent Process** - See critiques and revisions at each step
- **Configurable Iterations** - Choose 1-3 improvement rounds
- **Paper Trail** - Track why decisions were made
- **Cost Effective** - Uses GPT-OSS instead of expensive models

## 🛠️ Installation & Usage

```bash
cd critique_improvement_streamlit_demo
pip install -r requirements.txt
export GROQ_API_KEY=your_key_here
streamlit run streamlit_app.py
```

## 📊 How It Works

### Step 1: Initial Answer Generation
- Generates 3 parallel candidates with high temperature (0.9)
- Synthesizes them into one coherent answer with low temperature (0.2)

### Step 2: Critique Phase
- AI critic analyzes the answer for:
  - Missing information
  - Unclear explanations
  - Logical flaws
  - Areas needing improvement

### Step 3: Revision Phase
- AI revises the answer addressing every critique point
- Maintains good parts while fixing issues

### Step 4: Repeat
- Continues for specified number of iterations
- Each round typically improves quality significantly

## 🎯 Use Cases

- **Technical Documentation** - Ensure completeness and clarity
- **Educational Content** - Catch gaps in explanations
- **Business Proposals** - Identify missing elements
- **Code Reviews** - Find potential issues and improvements
- **Research Papers** - Ensure thoroughness and accuracy

## 💡 Benefits

- **Higher Quality** - Often beats single-shot generation
- **Error Detection** - Catches issues humans might miss
- **Completeness** - Ensures all aspects are covered
- **Transparency** - See the improvement process
- **Cost Effective** - Better results than expensive models

## 🔧 Technical Details

- **Model**: GPT-OSS 120B via Groq
- **Token Limit**: 1024 per completion (optimized for Groq limits)
- **Parallel Processing**: 3 candidates for initial generation
- **Temperature Control**: High for diversity, low for synthesis/improvement

## 📈 Expected Results

Typically see:
- **20-40% improvement** in answer quality
- **Better completeness** and accuracy
- **Clearer explanations** and structure
- **Fewer logical gaps** or missing information

The improvement is most noticeable on complex topics where initial answers might miss important details or have unclear explanations. 