A professional guide to choosing and monitoring AI agent architectures, covering single-agent, router, planner, and workflow patterns for production.
Senior Editor
4 min read
Build a prompt testing framework with datasets, scoring, version control, regression checks, and CI workflows for reliable LLM applications.
A practical RAG evaluation checklist for testing retrieval, groundedness, citations, answer quality, regressions, and production monitoring.
A practical guide to choosing short-term, long-term, and retrieval-based memory architectures for production AI agents.
A practical guide to choosing LangChain, LlamaIndex, or custom code for production-minded LLM apps.
A practical framework for comparing open source LLMs for self-hosted AI apps by hardware, speed, licensing, and production fit.
A reusable checklist for deploying LLM apps to the cloud with better architecture, secrets handling, scaling, and production readiness.
A practical guide to logs, traces, and metrics for monitoring LLM apps in production on a recurring schedule.
A practical guide to routing AI requests across small, fast, and premium LLMs using cost, latency, risk, and quality signals.
A practical framework for benchmarking AI coding models by repo understanding, patch quality, test generation, tool use, cost, and shipping impact.
A practical reference for defending RAG and tool-using AI apps against prompt injection with durable patterns and a review cycle.
A practical guide for comparing OpenAI, Anthropic, and Google APIs using real production criteria instead of hype.
A practical framework for comparing AI agent stacks on orchestration, memory, observability, tool use, and recovery in production.
A practical comparison framework for choosing AI coding assistants by quality, pricing, privacy, latency, and workflow fit.
A practical comparison guide to prompt-based app builders for internal tools, focused on governance, extensibility, deployment, and long-term fit.
A practical template for reducing LLM app latency across prompts, retrieval, streaming, model routing, and infrastructure.
A reusable AI guardrails checklist for production apps covering inputs, outputs, retrieval, agents, abuse prevention, and release reviews.
A practical guide to estimating AI app costs across tokens, retrieval, retries, caching, and model routing.
A practical framework for comparing embedding models for search, clustering, and recommendations by quality, multilingual support, hosting, and cost.
A practical guide to RAG evaluation metrics covering retrieval quality, citation support, latency, cost, and user trust beyond answer accuracy.
