Other Projects & Contributions

Collection of additional research projects, collaborations, and technical contributions

RAG Services and Strategy Evaluation

Project Summary: Comprehensive exploratory benchmark comparing off-the-shelf AWS internal vector databases against a configurable system using open-source embedding models and chunking strategies for RAG applications. The project conducted detailed evaluation of retrieval and generation performance, analyzing accuracy and latency metrics for state-of-the-art embedding models relevant to AWS Sagemaker Canvas customer use cases.

Overview

Duration: October - 2023

Role: Component Owner

Technologies: RAG, Information Retrieval, Vector Databases

Benchmark comparison of AWS internal vector databases and alternative strategies with embedding models and chunking approaches.

Contributions & Impact
  • • Detailed experiment design and execution for comprehensive RAG evaluation.
  • • Communication with non-technical stakeholders regarding design trade-offs and analysis outcomes.
Impact & Results

This deep-dive into RAG strategies provided scientific knowledge for the team regarding benefits and trade-offs, enabling the product team to choose an appropriate direction for implementation.

Improving Amazon Q for AutoML Chatbot Conversation Fidelity & Quality

Project Summary: Developed and significantly improved the conversational AI system for Amazon Q's AutoML chatbot in AWS Sagemaker Canvas. This project focused on enhancing conversation fidelity and quality through advanced system prompt engineering, enabling the chatbot to guide users more effectively through complex machine learning model training workflows while maintaining context and preventing conversation degradation.

Overview

Duration: June 2024 - August 2024

Role: Core Contributor

Technologies: LLM Prompt Engineering, LLM-as-a-Judge, Claude 3

This project involved designing a complex system prompt for a chatbot that guides users through the steps of training ML models in AWS Sagemaker Canvas (managed AutoML service).

Contributions & Impact
  • • Mini-benchmark for automated testing, with LLM-as-a-Judge for automated conversation quality monitoring.
  • • Engineered a major overhaul of the system prompt for the complex system, with significant iterative improvement.
  • • Designed the system prompt to allow the LLM (Claude 3) to attend to specific tasks and sub-tasks, without "context-rot".
Impact & Results

The enhanced system prompt significantly improved conversation quality and user experience, leading to better user retention and successful model training completion rates. The automated quality monitoring system continues to provide ongoing performance insights.

Data Notebook Coding Agent

Project Summary: Developed an intelligent coding agent for data notebooks that incorporates application-specific context engineering, iterative self-improvement, and adaptive generation logic. The agent demonstrates capabilities in code error correction through traceback analysis, scope maintenance, and generating solutions for complex problems using internal reasoning processes. Additionally, created a comprehensive benchmark system with memory management through summarization and LLM-as-a-Judge evaluation to assess the coding agent's performance.

Overview

Duration: September - 2025

Role: Component Owner

Technologies: LLM-as-a-judge, Agentic Context Engineering, Agentic Reasoning Loop

Contributions & Impact
  • • Functional coding agent in data notebook for Python and PySpark, for data science/engineer personas. Tools included static checkers such as `Python-AST` and `ruff` to enable self-corrective robust code, using the available context.
  • • Self-contained mini-benchmark, using LLM-as-a-Judge to enable test-driven development.
  • • Adaptive generation behavior to balance trade-off between cost and performance. Specifically, an adaptive procedural directive to handle complex or simple problems with maximum allocated budget of LLM calls.
Impact & Results

The prototype demonstrated the capabilities (complex problem solving) and limitations (latency) that could be achieved, along with problem-specific context engineering. The production version built upon the findings, and was launched at Re:Invent 2025.

References