LLM Model Routing in Practice: Cost, Latency, and Quality Driven by Evaluation
A practical guide to LLM model routing: signals, evaluation loops, cost budgets, fallback strategies, and go-live checks for dynamic model selection.
Articles
Searchable, citable, durable content on LLMs and AI agents. 128 articles, page 12 / 13.
-
-
Attention Sinks in Production: Using StreamingLLM for Long Session Generation
Explains the Attention Sink phenomenon, how StreamingLLM retains initial sink tokens and a recent window to reduce GPU memory pressure without fine-tuning, and provides production deployment boundaries, monitoring metrics, and a go-live checklist.
-
LLM-as-a-Judge in Production: Rubrics, Biases, and a Closed-Loop Regression Testing System
A practical guide to deploying LLM-as-a-Judge in production for RAG, Agent, and content generation systems, covering scoring modes, rubric design, bias sources, online regression, human review, and threshold governance.
-
Agent Observability in Practice: From Trace to Eval in a Production Loop
A practical guide to LLM Agent observability covering Trace, Span, evaluation, sampling, redaction, replay, and alerting to upgrade from log debugging to reproducible, auditable, and continuously improving quality engineering.
-
KV Cache Quantization in Practice: Managing Memory and Throughput for Long-Context Inference
KV Cache quantization isn't just about lowering precision; it's a complete serving governance strategy involving memory capacity, attention bandwidth, model accuracy, calibration data, and online fallback policies.
-
Continuous Batching in Practice: Why Traditional Fixed Batching Falls Short for LLM Serving
From fixed batching and iteration-level scheduling to PagedAttention, this article systematically explains the principles, benefits, trade-offs, and production deployment checklist for Continuous Batching in LLM inference serving.
-
MCP Tool Server Security Governance: From Easy Integration to Least-Privilege Execution
MCP standardizes LLM-tool connections but amplifies risks in permissions, identity, audit, and prompt injection. This post presents a practical security governance framework covering protocol boundaries, authorization models, execution isolation, and deployment checks.
-
MoE Expert Parallelism in Practice: Communication and Load Balancing for Sparse LLM Inference
A practical guide to Expert Parallelism for MoE inference: communication overhead, load balancing, parallel strategy combinations, and a pre-deployment checklist for production.
-
Multi-LoRA Serving in Practice: Serving Multiple Business Adapters with One Base Model
A practical guide to Multi-LoRA Serving: how to share one base model across multiple LoRA adapters for cost-effective, multi-tenant LLM inference.
-
Chunked Prefill in Production: Latency Governance for Long-Context Inference
A deep dive into how Chunked Prefill and Continuous Batching work together to manage latency in long-context LLM inference, covering scheduling principles, chunk size tuning, production metrics, and a deployment checklist.