Searchable, citable, durable content on LLMs and AI agents. 147 articles, page 2 / 15.
Red teaming loses value if run only once before launch. This article shows how to turn every confirmed security failure into a repeatable regression asset using versioned attack corpora, deterministic scoring, and CI quality gates with PyRIT, garak, and Promptfoo.
Rerankers often boost retrieval quality, but model upgrades can introduce domain regressions, tail latency, and candidate truncation issues. This article presents a production evaluation and release methodology—from hard negative construction and NDCG@10 offline gates to shadow re-ranking validation—to safely ship new ranking models.
Prompts directly change model behavior but are often deployed like ordinary config. This guide covers immutable versions, environment pointers, canary traffic, change validation, metric observation, and fast rollback for safe production releases.
After deploying multimodal LLMs, vision request tail latency often spikes due to image download, decode, vision encoding, and repeated processing. This article combines Dynamo, vLLM, and EPD practices to provide production governance methods including media decode offloading, Encoder Cache, dedicated Encoder Workers, input budgets, and stage-level observability.
Upgrading an embedding model is more than swapping a model name. This post breaks down production migration strategies—dual writes, background re-embedding, blue-green vector indexes, atomic alias switching, rollback windows, and incremental updates—to avoid recall drift and service disruption.
Learn how to reduce LLM cold start latency in production using local NVMe model caches, weight streaming, warm pools, and model-locality-aware scheduling. A practical engineering guide for KServe, vLLM, and GPU inference clusters.
A practical guide to fair scheduling for shared-GPU multi-tenant LLM inference. Learn why request-rate limits fail, and how token cost accounting, VTC, priority lanes, and tenant quotas balance throughput, isolation, and tail latency.
A practical guide for multi-tenant LLM inference platforms covering Multi-LoRA adapter residency in GPU/CPU tiered caches, LRU eviction, cold-load latency, affinity routing, capacity planning, monitoring, release governance, and launch checklists.
A production-grade guide for LLM inference services: using queue wait time, TTFT SLO, backpressure, and elastic autoscaling to handle traffic spikes, avoid scaling lag, tail latency blowups, and unbounded queuing. Includes key metrics, configuration, launch checklist, and common pitfalls.
A systematic guide to engineering data mixtures for LLM pretraining: using immutable Mixture Manifests to freeze domain weights and corpus versions, Token Budgets for precise accounting, and proxy model search, domain loss feedback, starvation protection, and replay gates to continuously calibrate capability structure. Turn mixture ratios from probabilistic parameters into auditable, reproducible training contracts.