RAG vs Long Context: Picking the Right Pattern
When retrieval beats a bigger context window — and when it doesn't. A practical comparison of latency, cost, freshness, and accuracy trade-offs.
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Writing on AI Systems & ML Careers
Practical notes on AI system design, agentic AI, MLOps, learning roadmaps, and career transition into AI/ML. Also on Medium, Substack, and LinkedIn.
How to Crack the AI/ML Interview in 2026
The four pillars of modern AI/ML interviews — fundamentals, system design, coding, and behavioral — with a six-week prep plan.
Read article →When to Fine-tune vs Prompt-engineer
A decision framework covering data needs, latency, cost, and maintenance — and when each approach actually pays off in production.
Read article →AI System Design Basics for Working Professionals
Data flow, model serving, latency constraints, and monitoring patterns for production AI — the decisions that show up in real projects.
Read article →Career Transition Playbook: Analyst to ML Engineer
Positioning, portfolio strategy, and interview prep to convert an analytics background into an ML engineering offer.
Read article →Roadmap Learning vs Random Tutorial Hopping
A phase-by-phase framework to build your AI/ML learning path — foundations, projects, and interview-oriented execution.
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