GenAI Lab
Building Enterprise Generative AI
Personal research and engineering lab for the next generation of Enterprise AI — Generative, Agentic, and beyond.
GenAI Lab
Personal research and engineering lab for the next generation of Enterprise AI — Generative, Agentic, and beyond.
Enterprise Focus Areas
Actively researching and engineering these capability areas for enterprise production deployment.
LLMs
Evaluating frontier model capabilities for enterprise use cases — reasoning, coding, multimodal, and long-context tasks.
Retrieval
Advanced RAG architectures: hybrid search, re-ranking, query transformation, contextual compression, and evaluation pipelines.
Agents
Multi-step reasoning agents with tool use, planning, and memory. ReAct, CoT, and autonomous decision systems for enterprise workflows.
Engineering
Systematic prompt design, few-shot learning, chain-of-thought prompting, and structured output generation for production systems.
Evaluation
Building evaluation frameworks for LLM outputs — accuracy, hallucination detection, faithfulness, and enterprise-specific metrics.
Governance
Enterprise AI governance: content safety, PII detection, audit trails, access controls, and responsible AI guardrails.
Multimodal
Vision-language models, document intelligence, and multimodal reasoning for enterprise content processing.
Knowledge
Knowledge graph-enhanced retrieval for complex multi-hop reasoning, entity relationships, and enterprise knowledge navigation.
Protocol
Exploring Anthropic's MCP standard for connecting LLMs to enterprise data sources, APIs, and tools in standardized agentic pipelines.
Personal Projects
Personal engineering projects to stay hands-on with frontier GenAI technology.
Project
LLM-powered resume tailoring system that analyzes job descriptions and generates role-specific resume content with ATS optimization.
Project
AI-powered interview preparation agent that generates tailored STAR-method answers and behavioral interview coaching.
Project
Modular RAG system with hybrid search, contextual compression, re-ranking, and automated evaluation — designed for enterprise adoption.
Project
Enterprise knowledge assistant combining knowledge graphs with RAG for complex multi-hop document reasoning and entity-based Q&A.
Project
Multi-agent system built on Model Context Protocol — agents that connect to enterprise APIs, databases, and tools through standardized interfaces.
Project
Self-hosted LLM platform using Ollama, LLaMA, and Mistral for privacy-sensitive enterprise use cases — full local inference stack.
Current Learning