Connected by a concept
Evaluation
10 articles across the publication.
A demo shows what worked once. Evaluation shows whether it works again.
How to Release an AI Change Without Guessing
Build a versioned release bundle, quality gates, limited rollout, and rollback plan for an AI assistant. Compare task quality alongside latency and failures.
When a Document Assistant Answers Without Evidence
Design a document assistant around authorization, source versions, evidence sufficiency, and citation checks rather than confidence alone.
When Caching Makes an AI Answer Fast and Wrong
Design scoped, versioned answer-cache keys and invalidation rules. Learn how stale evidence, permission changes, and concurrent fills affect correctness.
Why an AI Agent Creates the Same Ticket Twice
Follow a lost tool response through an agent, policy gate, action ledger, and idempotent service. Learn why a timeout cannot prove an action failed.
Your AI Model Is Fast. Why Are Users Still Waiting?
Trace a slow AI response from client to queue, prefill, decode, and delivery. Learn to investigate a traffic spike before choosing an intervention.
Build the baseline before the pipeline.
A dependency-free lexical retrieval experiment that makes tokenization, ranking, and limitations easy to inspect.
Inside a production RAG pipeline.
A reference design that puts source versions, authorization, retrieval quality, and failure handling around generation.
RAG gives a model evidence. Not certainty.
Separate retrieval from generation to understand where grounded answers succeed—and where they still fail.
The agent isn’t one model anymore.
Models propose. Tools execute. State remembers. The surrounding system makes the loop useful and accountable.
What does it actually mean for a machine to learn?
Start with the gap between memorizing examples and learning a pattern that survives new data.