About Me

Hi, I’m Denson. I’m an AI engineer passionate about building reliable, production-grade systems that leverage the power of Large Language Models and knowledge graphs without the hallucinations that plague so many modern AI applications.

I believe that the future of AI in enterprise isn’t about the fanciest models or the most impressive demos—it’s about building systems that teams can trust. When organizations depend on AI for decisions, reliability and accuracy aren’t nice-to-haves, they’re requirements.

What I Do

I specialize in solving the hard problems that emerge when deploying AI systems at scale:

LLM Systems Engineering

Designing and building reliable systems that leverage large language models for real-world applications. From prompt optimization to multi-turn conversations, I focus on creating systems that work consistently.

GraphRAG Architecture

Implementing knowledge graph-based retrieval augmented generation systems. By grounding LLMs in structured knowledge, I help systems provide accurate, verifiable answers backed by real data.

Evaluation & Testing

Creating robust frameworks to test, evaluate, and ensure the reliability of AI systems. Proper evaluation is foundational to building confidence in AI systems that teams can deploy.

Knowledge Extraction

Automating the process of extracting structured knowledge from unstructured data. Building the foundations that enable better retrieval, search, and reasoning in AI systems.

My Focus

I concentrate on problems that really matter:

Reducing hallucinations through better retrieval, grounding, and validation strategies
Making systems verifiable so teams understand why AI made a decision and can audit its reasoning
Building for production with proper evaluation frameworks, monitoring, and graceful degradation
Sharing knowledge about what works and what doesn’t in real-world AI applications

Tech & Expertise

LLMs & Foundational Models

OpenAI, Anthropic, open-source models, fine-tuning, prompt engineering

RAG & Knowledge Systems

Vector databases, knowledge graphs, semantic search, information retrieval

Evaluation Frameworks

Metrics design, automated evaluation, human evaluation, benchmarking

Python & Data Engineering

Python, async systems, data pipelines, APIs, system design

Production Deployment

Model serving, monitoring, observability, scalable systems

Research & Development

Staying current with AI research, experimenting with new approaches, learning in public

Philosophy

Hallucinations aren’t a bug in LLMs—they’re a fundamental feature of how these models work. The solution isn’t to find a better model; it’s to architect systems that reduce the opportunity for hallucinations to cause harm.

This means:

Grounding in Reality: Use knowledge graphs, databases, and structured retrieval to provide real information
Reducing Ambiguity: Design prompts and systems to minimize misinterpretation
Verifiable Answers: Ensure the system can point to sources and reasoning
Graceful Fallback: Build systems that know what they don’t know and ask for help

Let’s Talk

I’m interested in working with teams building AI systems that need to work reliably. Whether you’re starting with your first LLM application or scaling a complex multi-agent system, I’d love to help.

Reach out: GitHub

Email: denson@knowledgecrystal.com