Tamar H.

Responsibilities:

• Designed and implemented a clean, intuitive UI for the RAG Anything system, enabling seamless interaction with the retrieval-augmented pipeline and improving overall user experience.

• Research: prototype development for a personalized chatbot-alignment feature, exploring RLHF methodologies, evaluating HH-RLHF datasets, and fine-tuning Qwen models using TRL DPO to learn user-specific preferences through real-time feedback signals.

• Developed and evaluated query- and document-expansion strategies to improve semantic search accuracy, including generating expansion candidates with Gemma, enriching the knowledge graph with AI-domain question variations, and testing search performance across easy and hard real-world queries (e.g., technical, keyword-based, and time-sensitive questions).

• Designed and built the main server architecture using FastAPI, including full message-history management, asynchronous microservice communication, well-structured Pydantic models, custom middleware for logging and validation, and coroutine-based request handling to ensure scalable, high-performance operation across the entire RAG system.

• Deployed the main server and UI to production infrastructure, configured secure SSH-based server access, and integrated the frontend with the backend through Cloudflare routing. Ensured successful end-to-end connectivity, validated deployment stability, and delivered a live UI endpoint for team testing.

• Evaluated LLM performance over the MMLU benchmark by loading and filtering the dataset, generating chain-of-thought answers for 1,531 validation questions measuring accuracy and latency distribution, and comparing model performance with and without retrieval-based search. Implemented prompt-engineering improvements (e.g., reasoning-first format) and analyzed representative examples to assess quality and failure modes.

• Conducted LLM evaluation experiments on Natural Questions and HotpotQA datasets by prompting Gemma-3-1B and Gemma-3-4B to score question-answer pairs. Implemented multiple scoring strategies, including single-pass and averaged multi-pass ratings, analyzed outputs manually for 10+ examples to ensure reliability, and compared model accuracy across configurations.

• Implemented user-specific features in the RAG Anything UI and backend, including login with message-history persistence, document upload to named indices, and dynamic index selection for searches. Updated user database to track custom indices, integrated backend routes to handle document ingestion and indexing in OpenSearch, replaced static defaults with dynamic UI-driven values, and performed end-to-end testing of both frontend and backend components.