Research
Mechanisms, methods, limitations, and engineering implications without collapsing claims into headlines.
Balancing Cost, Latency, Reliability, and Security
Operational trade-offs must be measured at the successful user outcome, inside the same security and quality boundary.
The Complete Map of an LLM System
A production LLM system is a chain of contracts, not a model wrapped in a chat box.
Evaluating LLM Systems Without Guesswork
An evaluation is useful only when it changes a release, routing, or product decision.
Evaluation as a Control System for AI Products
Evaluation should connect product risks and user outcomes to measurable evidence, release policy, monitoring, and corrective action.
What Predictive Processing Can and Cannot Teach AI Engineers
Predictive processing offers a useful account of hierarchical inference and error correction, but it is not a shortcut from brain metaphor to system architecture.
Prompting vs RAG vs Fine-Tuning vs Tools
The right intervention follows the type of gap: instructions, knowledge, behavior, or action.
RAG from Ingestion to Grounded Citations
RAG quality is determined by the whole evidence path, not by adding a vector database.
Tokens, Context, Attention, and Inference
Four boundaries explain much of an LLM application's behavior: encoding, representation, finite context, and sequential generation.
Transformers as Systems: Tokens, Attention, Training, and Inference
A transformer becomes operationally understandable when architecture, training, inference, context, and serving constraints are traced as one system.