01
AI / Data Engineer
Aug 2026 – Present
Agentic RAG Knowledge Assistant
Evidence-aware retrieval-augmented generation system with multi-pass planning, atomic claim auditing, and bounded retries.
Problem: Standard feed-forward RAG architectures retrieve top-k chunks and immediately generate answers without verifying factual sufficiency. When queries are out-of-scope or contain false premises, naive generators fabricate plausible answers from parametric memory.
Solution: Built a closed-loop Agentic RAG pipeline in Python combining dense vector search and BM25 keyword matching via Reciprocal Rank Fusion (RRF, k=60). Implemented an explicit Evidence Auditor for atomic claim verification and dynamic PDF ingestion with SHA-256 Content-Addressable Storage (CAS) vector caching.
PythonOpenAI APIRank-BM25NumPyPydanticStreamlit
Key Fact
0.0%
Unanswerable Hallucination