Technical notes on building production AI systems, from RAG and agent orchestration to full-stack implementation and MLOps.
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Naive RAG retrieves the most relevant chunks for grounded answers, while Graph RAG maps entities and relationships to reason across connected knowledge and uncover deeper multi-hop insights.
Prompt engineers optimize how requests are phrased, while context engineers design the data and retrieval architecture that gives AI systems reliable, company-specific intelligence in production.
Read ArticleA personal progression from VS Code foundations, to Cursor for AI-assisted speed and deeper code understanding, to Antigravity for distraction-free focus during complex problem solving.
Read ArticleA practical breakdown of the shift from generative AI responses to agentic AI execution, covering reasoning loops, memory systems, tool use, multi-agent architectures, and production guardrails.
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