Welcome to GinkgoQ
GinkgoQ publishes practical engineering writing for teams building AI systems that need to work outside demos: agents, retrieval, document understanding, evaluation, validation, and production reliability.
What we publish
We write about the engineering layers around modern AI models: orchestration, structured state, retrieval, tool use, extraction, validation, observability, and evaluation.
The focus is practical system design. We care less about broad AI commentary and more about the decisions that determine whether an AI workflow can be debugged, audited, improved, and trusted.
Where to start
- Agentic AI architecture — Start with the two-part guide on reliable AI agents, orchestration, state, tools, retrieval, and evaluation.
- Document understanding — Read the extraction pipeline guide if you work with PDFs, contracts, invoices, forms, or domain documents.
- Research Notes — Use these for shorter analysis on RAG, domain intelligence, reliability, and evaluation.
- Latest posts — Check the homepage for the newest engineering guides and technical notes.
Finding what you need
Use search for topics such as agentic AI, RAG, retrieval, validation, document AI, production reliability, and evaluation. Blog posts are longer implementation guides. Research Notes are shorter technical arguments that clarify one system design problem at a time.
Understanding our content
Each article is designed to be useful on its own. When a topic is part of a series, the post will state where it fits. Code examples are intentionally small and architectural; they are meant to clarify system boundaries, not hide the design behind framework-specific boilerplate.