Searchable, citable, durable content on LLMs and AI agents. 147 articles, page 9 / 15.
A deep dive into production-grade sandbox runtimes for coding agents, covering filesystem boundaries, network allowlists, sensitive file isolation, approval escalation, container execution planes, audit trails, and a go-live checklist for secure LLM agent deployment.
A practical guide to transforming LLM inference capacity from reactive scaling to measurable, estimable, and alertable capacity engineering. Covers token throughput, queue depth, P99 TTFT, GPU headroom, and a pre-launch checklist to help teams identify bottlenecks and make quantitative scaling decisions.
This article explains how to transform long-running LLM Agent tasks from one-shot scripts into recoverable workflows, covering checkpoints, state machines, human-in-the-loop interrupts, retries, timeouts, idempotent side effects, and a production readiness checklist.
This article covers production governance for LoRA adapter caches in multi-tenant LLM serving, including dynamic loading, hot/cold tiering, eviction strategies, tenant quotas, version releases, and go-live checks to reduce first-token latency and GPU memory jitter.
A deep dive into production practices for hybrid retrieval routing in LLM RAG systems, covering query classification, BM25 and vector search fusion, RRF ranking, weight tuning, fallback governance, and a go-live checklist to reduce missed recalls and retrieval strategy failures.
This article covers engineering practices for production-grade long video QA, including frame sampling, shot segmentation, subtitle fusion, timestamp indexing, clip replay, evidence verification, and launch checklists to reduce missed keyframes and temporal ordering errors.
This article explains how to govern RAG retrieval index updates, covering blue-green indexes, dual-write, incremental backfill, alias switching, quality sampling, and rollback strategies to help teams reduce retrieval quality regression risk when documents, embedding models, or chunking rules change.
This article explains how to transform LLM red teaming from a one-time security test into a continuous regression gate, covering attack suite versioning, automated scanning, risk grading, false positive review, canary releases, and pre-release checks to help teams catch security regressions during model and prompt updates.
A deep dive into governing the cost of thinking in reasoning models: task classification, reasoning toggles, effort strategies, token caps, timeout fallbacks, and production checklists to balance quality, latency, and cost.
A practical guide to using multimodal LLMs for PDF and scanned document parsing in production. Covers layout chunking, table extraction, confidence auditing, human review, and launch validation to reduce hallucinations, missed fields, and misaligned parsing for complex documents like contracts, financial reports, and work orders.