Briefings
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Balancing Cost, Latency, Reliability, and Security
Operational trade-offs must be measured at the successful user outcome, inside the same security and quality boundary.
AI in Clinical Decision Support: Evidence, Workflow, Failure, and Governance
Clinical value depends less on a model demonstration than on intended use, evidence quality, workflow fit, human control, monitoring, and accountable escalation.
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.
Inside vLLM: KV Cache, Scheduling, and the Model Runner
vLLM shows how memory allocation, request scheduling, model execution, and distributed coordination turn a language model into a serving system.
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.