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User Query
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Hybrid Retrieval (BM25 + Dense Vectors)
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Authority Weighting (.edu/.gov +50%)
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Priority-Based Synthesis Layer (20+ Handlers)
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Cite-or-Abstain Validation
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TinyLlama-1.1B (Formatting Only)
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Answer with Citations| System | Citation Coverage | Fabrication Rate | Cost (10K queries) |
|---|---|---|---|
| CollegeAdvisor RAG | 100% | 0% | $200 |
| GPT-4 (pure LLM) | 0-30% | 3-8% | $2,000 |
| Claude 3.5 (pure LLM) | 0-30% | 3-8% | $1,500 |
| Generic RAG | 60-80% | 1-3% | $500 |
pip install transformers peft chromadb ollama1from rag_system.production_rag import ProductionRAG
2
3# Initialize RAG system
4rag = ProductionRAG()
5
6# Query with cite-or-abstain
7result = rag.query("What are UC Berkeley CS transfer requirements?")
8
9print(result.answer)
10print(f"Citations: {len(result.citations)}")
11for citation in result.citations:
12 print(f"- {citation.title}: {citation.url}")1@software{jiang2025collegeadvisor,
2 author = {Jiang, Shengbo},
3 title = {CollegeAdvisor RAG: Cite-or-Abstain Architecture for Hallucination-Free Advisory Systems},
4 year = {2025},
5 publisher = {Hugging Face},
6 url = {https://huggingface.co/your-username/collegeadvisor-rag}
7}