Searchable, citable, durable content on LLMs and AI agents. 128 articles, page 5 / 13.
Production strategies for LoRA adapter cache management in multi-tenant fine-tuning services: heat tiering, cold-start budgets, eviction policies, and a governance framework for stable model serving.
A systematic guide to building a multi-tenant quota gateway for shared LLM inference platforms, covering request rate limiting, token budgets, fair scheduling, cost attribution, and degradation strategies to prevent a few tenants from overwhelming the model pool and controlling billing risks.
A deep dive into production-grade autoscaling for LLM serving. Build a multi-layer control loop using queue depth, P95 latency, token throughput, and GPU utilization, with warm pools, admission control, cost boundaries, and cooldown strategies to meet inference SLOs under cost constraints.
A systematic guide to zero-downtime embedding index migration covering dual-writes, alias switching, backfill validation, and recall gates to prevent recall drift during model upgrades.
When model providers update, internal fine-tuning changes, or prompts are upgraded, online behavior can silently drift. This article covers the engineering practices of LLM canary releases—shadow traffic, compatibility gates, canary metrics, version registration, and auto-rollback—to help teams build a behavior-compatible release pipeline.
A practical engineering guide to designing a production-ready VLM image input pipeline. Covers resolution tiering, token budget estimation, multi-layer caching strategies, and a go-live checklist to control costs, latency, and quality for multimodal AI systems.
Multi-model provider integration goes beyond a unified SDK. This article systematically covers production governance for LLM adapter layers—from capability matrices, parameter normalization, and response contracts to streaming event adaptation, tool call lifecycles, and migration replay testing—helping teams reduce model migration costs and production risks.
A practical guide to building a production-grade content moderation system for LLM apps. Covers input/output/tool-result detection, threshold gray-scaling, false-positive reduction loops, audit logging, and rollout gating to balance safety and user experience.
A practical guide to building a reliability layer for LLM API clients in production, covering error classification, timeout boundaries, jittered backoff, retry budgets, request ledgers, degradation strategies, and deployment checklists.
A practical guide to versioning LLM fine-tuning datasets in production, covering sample lineage, train-test splits, evaluation gates, experiment tracking, rollback strategies, and pre-release checklists to prevent SFT degradation from dirty data, biased samples, or incorrect labels.