Writing
Notes on building software, shipping products, and lessons learned along the way.
A deep dive into the engineering complexities of building Retrieval-Augmented Generation systems that actually work in production, covering chunking, hybrid search, re-ranking, and evaluation.
A comprehensive exploration of MCP, the emerging open standard for connecting LLMs to tools and data sources, covering architecture, security, and real-world deployment.
A deep dive into building RAG systems: from chunking and embedding to hybrid search, evaluation, and the operational realities of deploying grounded LLM applications at scale.
A deep dive into retrieval-augmented generation systems, covering chunking, embeddings, vector search, hybrid retrieval, evaluation, and production best practices for AI engineers.
A thorough exploration of the architecture, algorithms, and operational best practices required to build scalable, reliable RAG pipelines for large language models in enterprise environments.
A deep dive into advanced RAG patterns—from chunking and hybrid search to agentic retrieval and production observability—that transform fragile demos into reliable knowledge systems.
A thorough exploration of RAG systems covering ingestion, embedding, hybrid search, re-ranking, evaluation, and production challenges for AI engineers.
A deep dive into building production-grade Retrieval-Augmented Generation systems, covering chunking, hybrid search, reranking, compression, evaluation, security, and agentic retrieval.
A practical guide to combining dense and sparse retrieval for robust question answering.
A deep dive into Anthropic's Model Context Protocol and how it standardizes AI-tool integration.
A practical look at how RAG bridges the gap between static LLMs and dynamic knowledge bases.
A deep dive into how MCP standardizes interactions between AI models and external tools, enabling more composable and maintainable agent architectures.
Pure vector search often misses precise keyword matches. Learn how hybrid retrieval combines BM25 and dense vectors to boost RAG accuracy.
Explore how to deploy and run modern LLMs on consumer-grade hardware using tools like llama.cpp and Ollama, balancing performance and resource constraints.
Explore how to architect robust LLM agents that plan, call tools, and manage state across complex multi-step tasks.
Model Context Protocol (MCP) is revolutionizing how we connect AI models to data sources. Learn why standardization is the key to scalable AI architectures.
Running LLMs locally is no longer just for researchers. Discover how Ollama makes local AI development accessible, secure, and highly cost-effective.
AI Agents represent the next frontier in AI Engineering. Discover how they differ from simple LLMs and how developers can build them to automate complex workflows.
Retrieval-Augmented Generation (RAG) is the key to stopping LLM hallucinations. Learn the best practices for building robust RAG pipelines.
After building multiple production apps, here are the patterns that actually worked (and the ones that didn't).
Not every real-time feature needs persistent connections. Here's how I built responsive UIs using smarter polling and caching strategies.
TypeScript can slow you down if misused. These are the patterns and configurations that have genuinely made me faster.
