Tag archive
A spreadsheet-ready expected-cost model for agent workflows that includes retries, tool-call fanout, context growth, and caching. Plus hard budgets you can actually enforce.
Monitoring an AI gateway is crucial for performance, cost, and compliance. This post details eight...
Teams increasingly require robust AI gateways to manage complex LLM workloads. This article explores...
Expanding your context window from 4K to 128K tokens doesn't fix RAG — it masks retrieval failures with coherent-sounding hallucinations. Here's the measurement framework that actually works.
Enterprise LLM deployments face common challenges related to governance, security, performance,...
Most AI agent failures trace back to missing evals. Here's the 3-level framework — unit tests, LLM-as-judge, and online evaluation — that actually works in production.
Real TTFT and throughput data across 10+ models, where latency breaks user experience, and 6 architectural levers to hit sub-500ms budgets in production without sacrificing quality.
Stop guessing which LLM technique to use. A 2026-updated decision matrix with real cost figures, concrete examples, and a clear flowchart for when fine-tuning beats RAG, when RAG beats both, and when prompt engineering alone is the right answer.
Why production AI is 90% harness and 10% model. And what most pilots miss.
Production AI agents fail silently without proper memory and state management. Here's the four-tier memory architecture, durable resumption patterns, and framework implementations in LangGraph, CrewAI, and raw Python.
Production-tested patterns for building safe, reliable AI agents that won't destroy your data. Real lessons from the trenches.
In 2026, the most common failure mode I see among engineering teams building with AI isn't a bad...