Engineering notes
Deep dives on AI architecture and updates on what we are building.
Serving LLMs at Scale: Inference Architecture & Cost Optimization
Training gets the headlines, but inference is where the bill arrives — every request, every day. Serving large language models affordably and quickly is an architecture discipline of its own. These are the levers that matter most.
Read more →Architecting Agentic AI: Orchestrating Multi-Agent Workflows
An agent is a language model that can plan, call tools, observe results, and decide what to do next. Chaining a few prompts is simple; building an agentic system that is reliable, bounded, and debuggable is an architecture problem. Here is how we approach it.
Read more →Designing Production-Grade RAG Systems
Retrieval-augmented generation (RAG) has become the default way to ground large language models in your own data. The demo is easy; the production system is not. The gap is architecture — how you ingest, retrieve, rank, and evaluate.
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