Deep dives into artificial intelligence tools, practical guides, and expert perspectives on the technologies shaping tomorrow.
21 articles found in AI Engineering
Docs drift is a maintenance problem, not a writing problem. How Fern, Mintlify, Swimm, and ReadMe attack it in 2026, the three-approach map, and the drift test that separates tools that hold up from tools that stay quiet.
LLMOps is the practice of running an LLM feature reliably for real users. This guide covers the evaluate-deploy-observe-iterate loop, how it extends MLOps, which part to build first and a six-step starter loop.
AI agent observability is a saved record of every step, tool call, token and error in a run. Here is what to record, a copy-paste JSONL trace log, and when a platform is worth it.
LangGraph, CrewAI and the OpenAI Agents SDK compared on control, memory, tracing, model support and license, with a three-question rule for picking your first agent framework.
Eight moves that make your pages easier for AI assistants to quote, each labelled measured, official or inferred, plus what to skip and how to measure citations.
Invalid JSON, missing fields, and silent regressions break production AI pipelines. Here is the reliability stack: constrained decoding, schema validation, and cross-model prompt regression testing with tools like Promptfoo, Outlines, and Instructor.
OpenAI is shutting down its Evals platform and is acquiring Promptfoo. Here is the current eval tool landscape, from Braintrust and LangSmith to DeepEval and RAGAS, plus a fixed-benchmark starter suite with factual, adversarial, structured-output, and tool-use cases you can build this week.
AI API testing tools generate tests from your spec, from recorded traffic, or through an agent in your workspace. Here is what Schemathesis, Keploy, Postman Agent Mode and Kiro each catch, what they miss, and how to layer them in CI.
Stop regex-ing prose into data and retrying failed parses. Learn the full ladder of LLM structured output, from prompting and JSON mode to schema-enforced structured outputs and tool calling, with vendor-by-vendor limits, latency gotchas, and when not to force a schema.
Function calling (tool use) is the loop where an LLM proposes a structured tool call, your app executes it, and the model answers with the result. Learn schemas, parallel calls, RAG vs agents, and safety defaults.
Learn how to evaluate AI agents the practical way: the four dimensions to measure, how to build an eval set from real traces, how LLM-as-a-judge scoring works, and a copy-paste harness with a no-regression loop to run before every change.
A practical map of AI agent memory: the four memory types, the wiring that makes each real (notes files, vector stores, memory layers), the seven reasons agents still forget, and a minimal setup you can copy today. Covers LangGraph, Letta, and Mem0.
