Computer Science · intermediate
Production-Ready RAG Pipeline with Source Attribution
A RAG system with a citation engine that highlights exact source text, a faithfulness evaluator, and hybrid search.
Project overview
Employers are moving past "chatbots" to "reliable knowledge systems." Showing you can measure and guarantee the accuracy of an LLM's response is a top-tier engineering skill.
Real-world use case
A legal firm needs to query thousands of case files and requires 100% accurate citations to the original documents.
Tools and technologies
- Python
- LangChain
- ChromaDB
- Ragas
- Docker
Skills you can demonstrate
Vector DBs, Semantic Search, LLM Evaluation
Build roadmap
- Set up the base RAG
LangChain + ChromaDB for local speed. Ingest a sample corpus (e.g., legal filings, research papers). - Implement hybrid search
Combine dense (vector) search with sparse (BM25). Merge results with Reciprocal Rank Fusion for better recall. - Add a re-ranking step
Use a Cross-Encoder like ms-marco-MiniLM to re-rank the top 20 results down to the best 5. Precision jumps significantly. - Build the citation engine
Track the exact character offsets of each chunk in the source. Highlight them in the UI when the LLM cites them. - Add the faithfulness evaluator
Use Ragas to measure answer_relevancy, faithfulness, and context_recall. Generate test questions synthetically from your docs. - Publish the evaluation report
Create eval_report.json and a README section comparing chunking strategies, embedding models, and retrieval configs. This is the portfolio differentiator.
Ways to extend the project
Use AI to automatically generate "synthetic test questions" from your documents to stress-test your RAG pipeline.
How to showcase your work
- GitHub: Include an eval_report.json showing faithfulness scores.
- LinkedIn: Video demo showing the "Source Attribution" feature in action.
- Resume: Highlight production RAG + evaluation metrics.