Problem
Institutional research teams need dependable access to large, heterogeneous knowledge collections without relying on ungrounded model responses.
Context
The current site describes this work as a full-stack LLM assistant for retrieval, question answering, and knowledge automation.
My role
Led development of retrieval, reranking, agentic workflow, and evaluation components.
Constraints
- Retrieval quality must remain inspectable.
- Responses need grounding and safety checks.
- The system must support maintainable, production-oriented workflows.
TODO_REVIEW: Confirm data scale, deployment environment, collaborators, and any public confidentiality limits.
Architecture
The source material identifies hybrid retrieval, metadata indexing, cross-encoder reranking, task decomposition, memory modules, orchestration, and automated evaluation.
TODO_REVIEW: Add an approved architecture diagram and exact component stack.
Technical decisions
- Combined retrieval methods before reranking.
- Added explicit workflow orchestration for multi-step tasks.
- Evaluated hallucination, relevance, and safety behavior.
Trade-offs
TODO_REVIEW: Document latency, retrieval depth, model selection, hosting, and
cost trade-offs.
Results
TODO_REVIEW: Add verified qualitative outcomes, benchmark definitions, and
approved metrics. No result numbers were available in the source repository.
Screenshots
TODO_REVIEW: Add approved product screenshots or a redacted demo.
Related links
TODO_REVIEW: Add public code, demo, article, or institutional links where
permitted.