I write about the engineering behind language models, retrieval systems, and data infrastructure.
Technical deep dives and implementation walkthroughs, from the underlying concepts to working code.
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A cleaner mental model for LLM inference: prompt templating, tokenization, scheduling, prefill, KV cache, decode, and the split between the inference engine and the model.
Read the articleConnect the ideas
Ten articles for software engineers on how RAG systems work and fail. Follow worked examples through parsing, indexing, retrieval, context assembly, grounded answers, evaluation, and security.
Understand how prompts become tokens, how attention and KV cache work, and how serving engines scale inference.
Explore this pathLearn how agents use tools, how MCP connects systems, and how multi-agent workflows coordinate.
Explore this pathBuild local chat applications, RAG pipelines, reranking workflows, and natural-language data tools.
Explore this pathLearn DataFrame operations, SQL tuning, Spark optimization, and structured streaming.
Explore this pathFresh from the notebook
Carry authorization through retrieval, context, caches, and citations. Define trust boundaries, revocation behavior, and operational controls.
Separate retrieval coverage, context quality, and answer support. Calculate ranking metrics and design reproducible regression gates.
Separate schema validity, citation identity, and factual support; design bounded repair, honest abstention, and streaming behavior around those distinct guarantees.
Translate questions into bounded retrieval plans, preserve metadata constraints, and verify SQL result semantics with a worked incident-table example.
Work through reciprocal rank fusion, distinguish full-text search from BM25, and evaluate reranking and corrective retrieval without treating scores as confidence.
Turn retrieved candidates into useful context by preserving qualifiers, tracking expansion provenance, budgeting tokens, and separating image retrieval from visual answering.
An ongoing practice
Always learning.
Still building.
