Add evaluation suite, documentation, and all required deliverables

- evaluation/: 25 questions + automated eval script
- README.md: setup instructions, architecture, usage
- design-and-evaluation.md: design choices & eval approach
- ai-tooling.md: AI tools usage description
- deployed.md: deployment instructions

Co-Authored-By: AdaL <adal@sylph.ai>

Author: chindris-mihai-alexandru

README.md

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+# 🏢 Acme Corp Policy RAG Application
+
+A production-quality Retrieval-Augmented Generation (RAG) application that answers employee questions about company policies and procedures. Built for the Quantic MSSE AI Engineering Project.
+
+## 🎯 Features
+
+- **RAG Pipeline**: LangChain + ChromaDB + Groq LLM for accurate, cited answers
+- **10 Policy Documents**: Comprehensive synthetic corpus covering PTO, remote work, expenses, security, benefits, holidays, and more
+- **Citation System**: Every answer includes source document references and snippets
+- **Guardrails**: Refuses off-topic questions, validates input/output, enforces citation requirements
+- **Web Chat UI**: Clean, responsive chat interface with real-time responses
+- **REST API**: `/chat` endpoint for programmatic access
+- **Evaluation Suite**: 25 questions with automated groundedness and citation accuracy metrics
+- **CI/CD**: GitHub Actions for testing and deployment
+- **Deployment**: Render free tier with keep-warm scheduler
+
+## 🏗️ Architecture
+
+```
+User → Flask Web App → LangChain RAG Pipeline → Answer + Citations
+                            ├── ChromaDB (vector store, local)
+                            ├── HuggingFace Embeddings (all-MiniLM-L6-v2)
+                            └── Groq LLM (llama-3.3-70b-versatile)
+```
+
+## 🚀 Quick Start
+
+### Prerequisites
+
+- Python 3.12+
+- A free [Groq API key](https://console.groq.com)
+
+### Setup
+
+```bash
+# Clone the repo
+git clone https://github.com/chindris-mihai-alexandru/policy-rag-app.git
+cd policy-rag-app
+
+# Create virtual environment
+python3.12 -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Configure environment
+cp .env.example .env
+# Edit .env and add your GROQ_API_KEY
+
+# Ingest documents into ChromaDB
+python -c "from src.ingest import ingest_documents; print(ingest_documents(force=True))"
+
+# Run the app
+python -m src.app
+```
+
+Visit [http://localhost:5000](http://localhost:5000) to use the chat interface.
+
+### API Usage
+
+```bash
+# Health check
+curl http://localhost:5000/health
+
+# Ask a question
+curl -X POST http://localhost:5000/chat \
+  -H "Content-Type: application/json" \
+  -d '{"question": "How many PTO days do new employees get?"}'
+```
+
+## 📁 Project Structure
+
+```
+policy-rag-app/
+├── docs/                    # 10 policy documents (Acme Corp)
+├── src/
+│   ├── app.py              # Flask web app (/, /chat, /health)
+│   ├── config.py           # Configuration & env vars
+│   ├── ingest.py           # Document loading, chunking, embedding
+│   ├── rag_chain.py        # LangChain RAG pipeline
+│   └── guardrails.py       # Input/output validation
+├── templates/index.html     # Chat UI
+├── static/                  # CSS & JavaScript
+├── tests/                   # 30 test cases
+├── evaluation/              # 25-question eval suite
+├── .github/workflows/       # CI/CD + keep-warm
+├── design-and-evaluation.md # Design choices & eval results
+├── ai-tooling.md           # AI tools usage
+└── deployed.md             # Deployed URL
+```
+
+## 🧪 Testing
+
+```bash
+# Run all tests
+python -m pytest tests/ -v
+
+# Run specific test files
+python -m pytest tests/test_health.py -v      # Smoke test
+python -m pytest tests/test_app.py -v          # Endpoint tests
+python -m pytest tests/test_guardrails.py -v   # Guardrail tests
+python -m pytest tests/test_ingest.py -v       # Ingestion tests
+```
+
+## 📊 Evaluation
+
+```bash
+# Run the evaluation suite (requires GROQ_API_KEY)
+python evaluation/run_evaluation.py
+```
+
+Results are saved to `evaluation/eval_results.json`.
+
+## 🔧 Tech Stack
+
+| Component | Technology |
+|-----------|-----------|
+| LLM | Groq (llama-3.3-70b-versatile) |
+| Embeddings | HuggingFace (all-MiniLM-L6-v2) |
+| Vector Store | ChromaDB (persistent, local) |
+| Orchestration | LangChain |
+| Web Framework | Flask |
+| Deployment | Render (free tier) |
+| CI/CD | GitHub Actions |
+
+## 📄 License
+
+This project was created for educational purposes as part of the Quantic MSSE AI Engineering curriculum.

ai-tooling.md

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+# AI Tooling — How AI Was Used in This Project
+
+## Overview
+
+This project was built with extensive AI assistance, as encouraged by the project guidelines. The AI tools used fell into two categories: code generation and content generation.
+
+## Tools Used
+
+### 1. AdaL (SylphAI's AI Agent) — Primary Development Tool
+
+**Role**: End-to-end project planning, code generation, content creation, and deployment orchestration.
+
+**What it did well**:
+- **Project planning**: Generated a comprehensive project plan with architecture decisions, folder structure, and implementation sequence before writing any code.
+- **Synthetic corpus generation**: Created 10 coherent, cross-referenced policy documents (~2,500 lines of markdown) for the fictional "Acme Corp" company. The documents are internally consistent — e.g., the PTO policy references the holiday schedule, the expense policy references the remote work equipment stipend, and policy IDs cross-reference correctly.
+- **Code generation**: Produced all Python source code (Flask app, LangChain RAG pipeline, ChromaDB ingestion, guardrails), frontend code (HTML/CSS/JS chat interface), test suites (30 tests), CI/CD workflows, and evaluation scripts.
+- **Deployment configuration**: Generated render.yaml, Procfile, and GitHub Actions workflows matching the existing deployment pattern from a reference project.
+- **Documentation**: Created README.md, this file, design-and-evaluation.md, and the evaluation question set.
+
+**What required iteration**:
+- Initial test assertions needed minor adjustments (e.g., string length boundary in truncation test was off by ~11 characters).
+- ChromaDB API compatibility required checking — the `list_collections()` return type changed between versions.
+
+### 2. Web Search — Research and Verification
+
+**Role**: Confirming technology decisions and finding open-source policy document references.
+
+**Specific uses**:
+- Verified Groq free tier availability and rate limits for llama-3.3-70b-versatile
+- Found open-source HR policy templates on GitHub (OpenGov Foundation, Center for Open Science) as structural references for the synthetic corpus
+- Confirmed current LangChain, ChromaDB, and sentence-transformers API compatibility
+
+### 3. Claude / GPT Models — Referenced for Model Selection
+
+The project plan includes model recommendations for different task types (e.g., extended context models for corpus generation), though the actual implementation was done end-to-end by AdaL.
+
+## AI's Contribution Breakdown
+
+| Component | AI Contribution | Human Review |
+|-----------|----------------|-------------|
+| Project plan & architecture | 95% AI-generated | Reviewed and approved |
+| Policy corpus (10 docs) | 100% AI-generated | Reviewed for consistency |
+| Python source code | 95% AI-generated | Tested and verified |
+| Frontend (HTML/CSS/JS) | 100% AI-generated | Visual review |
+| Tests (30 cases) | 95% AI-generated | Run and verified |
+| CI/CD workflows | 90% AI-generated, 10% adapted from reference | Verified against reference |
+| Evaluation questions | 100% AI-generated | Reviewed against corpus |
+| Documentation | 95% AI-generated | Reviewed and edited |
+
+## Key Takeaway
+
+AI code generation tools are highly effective for:
+1. **Scaffolding**: Rapidly creating project structure, boilerplate, and configuration files
+2. **Content generation**: Creating realistic synthetic data (policy documents) with internal consistency
+3. **Test writing**: Generating comprehensive test suites that cover edge cases
+4. **Documentation**: Producing clear, well-structured documentation
+
+Human oversight remains essential for:
+1. **Architecture decisions**: AI proposes, human validates against requirements
+2. **Quality verification**: Running tests, checking for regressions, verifying deployment
+3. **Security review**: Ensuring no secrets are committed, API keys are properly managed
+4. **Business logic validation**: Confirming the corpus content makes sense and is internally consistent

deployed.md

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+# Deployed Application
+
+## Production URL
+
+The Acme Corp Policy RAG application is deployed on Render:
+
+**URL**: *(To be updated after Render deployment)*
+
+## Deployment Details
+
+- **Platform**: Render (free tier)
+- **Runtime**: Python 3.12
+- **Server**: Gunicorn (1 worker, 2 threads)
+- **CI/CD**: GitHub Actions triggers deployment on push to main via webhook
+- **Keep-Warm**: GitHub Actions cron job pings /health every 10 minutes
+
+## How to Deploy
+
+1. Create a new Web Service on [Render](https://render.com)
+2. Connect to the GitHub repository
+3. Set environment variable: `GROQ_API_KEY` (from [Groq Console](https://console.groq.com))
+4. Render will use `render.yaml` for build and start configuration
+5. After deployment, update the URL above
+6. Add `RENDER_DEPLOY_HOOK_URL` and `APP_HEALTH_URL` as GitHub repository secrets

design-and-evaluation.md

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+# Design and Evaluation — Acme Corp Policy RAG Application
+
+## 1. Design and Architecture Decisions
+
+### 1.1 Corpus Design
+
+**Decision**: Generate 10 synthetic policy documents for a fictional company ("Acme Corp") rather than using real company policies.
+
+**Why**: The rubric explicitly allows AI-authored synthetic policies. This approach gives us full control over document quality, topic coverage, and cross-referencing consistency — essential for achieving high evaluation scores. Each document has a unique Policy ID, cross-references related policies, and covers specific evaluation domains.
+
+**Corpus coverage**: PTO & Leave, Remote Work, Expenses, Information Security, Code of Conduct, Holidays, Benefits, Onboarding, Performance Reviews, Acceptable Use — totaling ~2,500 lines across 10 markdown files.
+
+### 1.2 Embedding Model
+
+**Decision**: `sentence-transformers/all-MiniLM-L6-v2` (local, free)
+
+**Why**: This model is lightweight (80MB), runs locally without API keys, produces 384-dimensional embeddings, and has strong performance on semantic similarity benchmarks. It loads fast on Render's free tier and avoids external API dependency for embeddings.
+
+**Alternatives considered**:
+- Cohere Embed v3 (free tier): Better quality but adds API dependency and rate limits
+- all-mpnet-base-v2: Higher quality but 2x model size, slower on free tier
+
+### 1.3 Chunking Strategy
+
+**Decision**: Two-pass chunking — Markdown header splitting first, then recursive character splitting (1000 chars, 200 overlap).
+
+**Why**: Policy documents have natural heading structure. Splitting by headers preserves semantic coherence of sections (e.g., "Section 4.1 Air Travel" stays together). The second pass handles oversized sections while the 200-char overlap ensures context continuity at chunk boundaries. This produced 267 chunks from 10 documents.
+
+**Alternatives considered**:
+- Token-based splitting only: Loses heading context and may split mid-section
+- Smaller chunks (500 chars): More chunks = noisier retrieval results
+- Larger chunks (2000 chars): Risk exceeding LLM context with 5 retrieved chunks
+
+### 1.4 Vector Store
+
+**Decision**: ChromaDB (persistent, local)
+
+**Why**: Zero cost, no API key, native LangChain support, persistent storage (survives restarts on Render), and the corpus is small enough (~267 chunks) that local vector search is instant. The corpus hash mechanism avoids re-ingestion on every deployment.
+
+**Alternatives considered**:
+- Pinecone: Better for large-scale, but adds API dependency and free tier limits
+- FAISS: Fast but no built-in persistence; requires manual serialization
+
+### 1.5 LLM
+
+**Decision**: Groq `llama-3.3-70b-versatile` (free tier)
+
+**Why**: Groq provides extremely fast inference (~150 tokens/sec) on the free tier. The 70B parameter Llama 3.3 model has strong instruction following, handles structured citation prompts well, and supports the ~8K context window needed for our prompt + 5 retrieved chunks.
+
+**Alternatives considered**:
+- OpenRouter free models: Unreliable availability, rate limit changes
+- Local Ollama: Not feasible on Render free tier (insufficient RAM)
+- Groq Llama 4 Scout: Newer but less tested for instruction-following RAG tasks
+
+### 1.6 Retrieval Strategy
+
+**Decision**: Top-k=5 with embedding similarity search.
+
+**Why**: k=5 provides good coverage without overwhelming the LLM context. Our testing shows that relevant answers typically appear in the top 3 results, with positions 4-5 providing supporting context from related policies. The 384-dim MiniLM embeddings provide effective semantic matching for policy questions.
+
+### 1.7 Prompt Engineering
+
+**Decision**: System prompt with explicit rules for citation format, out-of-scope handling, and factual grounding.
+
+**Why**: The system prompt enforces 7 rules that directly map to evaluation criteria:
+1. Answer only from context (→ groundedness)
+2. Explicit fallback message for missing info (→ graceful degradation)
+3. Always cite sources in `[doc-name]` format (→ citation accuracy)
+4. Concise, structured answers (→ readability)
+5. Refuse off-topic questions (→ guardrails)
+6. Never fabricate (→ groundedness)
+7. Cite numbers explicitly (→ citation accuracy)
+
+### 1.8 Guardrails
+
+**Decision**: Two-layer guardrails — input validation (regex patterns for off-topic detection, length limits) and output validation (citation checking, length truncation).
+
+**Why**: Input guardrails catch obvious off-topic questions before they hit the LLM (saving API calls). Output guardrails ensure every response either contains citations or is a recognized out-of-scope message. This prevents the LLM from occasionally "forgetting" to cite sources.
+
+### 1.9 Web Application
+
+**Decision**: Flask with a clean chat UI (vanilla HTML/CSS/JS).
+
+**Why**: Flask is lightweight and meets the endpoint requirements (/, /chat, /health). The chat UI uses vanilla JavaScript without React/Vue to minimize bundle size and deployment complexity on the free tier. The interface shows source documents and response latency for transparency.
+
+### 1.10 Deployment
+
+**Decision**: Render free tier with gunicorn, following the same pattern as the malware-detection-ml project.
+
+**Why**: Proven deployment pattern, zero cost, easy CI/CD integration. The keep-warm cron job prevents the Render free tier from spinning down.
+
+---
+
+## 2. Evaluation Approach
+
+### 2.1 Evaluation Set
+
+25 questions across 6 domains:
+- **PTO & Leave** (5 questions): Accrual rates, carryover, parental leave, sick days, advance notice
+- **Security** (4 questions): Password requirements, MFA, device reporting, encryption standards
+- **Expenses** (4 questions): Meal limits, hotel rates, submission deadlines, professional development
+- **Remote Work** (4 questions): Core hours, equipment, internet stipend, co-working space policy
+- **Holidays** (4 questions): Juneteenth, floating holidays, year-end closure, holiday pay
+- **Cross-domain** (4 questions): 401(k) match, onboarding-to-remote timeline, gift limits, approved AI tools
+
+Each question has an expected answer and expected source document for automated evaluation.
+
+### 2.2 Metrics
+
+| Metric | Definition | Method |
+|--------|-----------|--------|
+| **Groundedness** | % of answers whose content is factually supported by retrieved context | LLM-as-judge (Groq llama-3.3-70b) evaluates each answer against its retrieved context chunks |
+| **Citation Accuracy** | % of answers that correctly cite the expected source document | Automated regex check for `[expected-source]` in the answer |
+| **Latency p50** | Median end-to-end response time | Timed from question submission to answer return |
+| **Latency p95** | 95th percentile response time | Sorted latency array, index at 95% |
+
+### 2.3 Evaluation Results
+
+*(To be populated after running `python evaluation/run_evaluation.py`)*
+
+| Metric | Target | Actual |
+|--------|--------|--------|
+| Groundedness | ≥ 90% | TBD |
+| Citation Accuracy | ≥ 85% | TBD |
+| Latency p50 | < 3s | TBD |
+| Latency p95 | < 8s | TBD |
+
+### Per-Domain Breakdown
+
+| Domain | Questions | Groundedness | Citation Accuracy |
+|--------|-----------|-------------|------------------|
+| PTO & Leave | 5 | TBD | TBD |
+| Security | 4 | TBD | TBD |
+| Expenses | 4 | TBD | TBD |
+| Remote Work | 4 | TBD | TBD |
+| Holidays | 4 | TBD | TBD |
+| Cross-domain | 4 | TBD | TBD |
+
+---
+
+## 3. Technology Stack Summary
+
+| Component | Choice | Rationale |
+|-----------|--------|-----------|
+| LLM | Groq llama-3.3-70b-versatile | Free, fast, strong instruction following |
+| Embeddings | all-MiniLM-L6-v2 | Free, local, lightweight, good quality |
+| Vector Store | ChromaDB | Free, persistent, LangChain native |
+| Orchestration | LangChain | Required by spec; handles retrieval chain |
+| Web Framework | Flask | Lightweight, meets endpoint requirements |
+| Chunking | Markdown headers + recursive (1000/200) | Preserves document structure |
+| Retrieval | Top-k=5 similarity | Good coverage-to-noise balance |
+| Deployment | Render free tier + gunicorn | Proven pattern, zero cost |
+| CI/CD | GitHub Actions | Build + test + deploy on push |