Traditional provisioning models fail for LLM workloads. Here's the forecasting methodology that accounts for token burstiness, KV cache pressure, and why GPU utilization is a misleading signal.
Real-time AI suggestions paradoxically increase cognitive load by shifting work from generation to verification. Here's the research and the design patterns that actually help.
Context compaction silently drops the failure records and constraint information that prevent agents from re-attempting operations they already know won't work. Here's how to design around it.
Composing retrievers, rerankers, code interpreters, classifiers, and LLMs into pipelines that reliably outperform any single component — and the emergent failure modes that appear when you don't engineer the seams.
Teams routinely pack codebases, histories, and documents into context and absorb the cost and quality degradation without measuring it. Here's why LLM context deserves the same explicit management as CPU registers — and how to build eviction policies that make it work.
What actually happens when your LLM context fills up mid-session, why most frameworks handle it badly, and the summarization, selective retention, and externalization patterns that keep long-lived conversations coherent.
HTTP error rates can't detect behavioral regression in LLM upgrades. Here's how to run blue/green and canary deployments with behavioral divergence as the real rollback signal.
UX writing in system prompts, error messages, and capability disclosures directly shapes model behavior and user trust — in ways most engineering teams never measure.
Most RAG failures are diagnosed at query time but caused at index time. A technical guide to the chunk size, overlap, hierarchy, and metadata decisions that silently determine retrieval quality.
Vector ANN search finds semantically adjacent chunks, not necessarily the most useful ones. Layer cross-encoder reranking, MMR, and BM25 hybrid scoring to close the retrieval quality gap—with latency math that tells you when it pays off.
Traditional ML degrades gracefully on noisy data. LLMs hallucinate confidently, corrupt vector stores, and propagate errors downstream with apparent authority. Here's how to measure and mitigate the data quality tax.
When an agent runs for hours, knowing where it is—and whether it's still on track—becomes a first-class engineering problem. These are the patterns that solve it.