Articles
Technical deep-dives on AI systems, LLM internals, and engineering patterns — with code you can run.
Building Reliable AI Agents: Tool Use, Error Recovery, and State Management
A production engineer's guide to AI agents that actually work — structured tool calling, graceful error recovery, conversation state, and the hard lessons from shipping agents.
Fine-tuning vs RAG: The Engineering Decision Framework
When to fine-tune a model, when to use RAG, and when to combine them — a practical decision framework with cost analysis and real-world tradeoffs.
LLM Inference Optimization: KV Cache, Batching, and Quantization
The engineering playbook for making LLM inference fast and cheap — KV cache mechanics, continuous batching, speculative decoding, and quantization tradeoffs.
Vector Databases in Production: HNSW, IVF, and Choosing the Right Index
A deep technical comparison of HNSW and IVF vector indices — how they work, when each shines, and the operational tradeoffs that matter at scale.
Prompt Engineering Patterns Every Developer Should Know
Practical, battle-tested patterns for writing prompts that produce reliable, structured output from LLMs — with code examples you can copy and ship.
Understanding Context Windows in LLMs
A deep technical dive into how large language models manage context — token limits, KV cache, attention complexity, and what it means for your applications.