11 articles tagged #AI Engineering
Function calling enables LLM agents to interact with APIs, databases, and external tools, but poorly formed arguments can cause unexpected failures. Learn why tool calls break, common causes of invalid parameters, and proven techniques for building reliable AI agents.
Microsoft Build 2025 dropped a dense stack of announcements. Here's the practical breakdown: which new tools, APIs, and platform changes actually affect your workflow today and where to start first.
Semantic caching can dramatically reduce LLM costs and response times, but many teams discover that seemingly identical questions still bypass the cache and trigger expensive model calls. Learn why semantic cache misses occur, how embedding similarity works, and how to build production-ready caching
Ensemble learning is designed to improve predictive performance by combining multiple machine learning models. Yet many teams discover that their production ensemble performs worse than even its weakest individual model. Learn why this happens, how production environments differ from offline
Google I/O 2025 dropped a dense payload of developer tools, APIs, and model updates. Here's a clear-eyed breakdown of what actually matters for your workflow right now — and what you can safely ignore until later.
GPT-4.1 and GPT-4o look similar on paper, but they behave very differently on coding tasks, long-context retrieval, and API costs. Here's exactly how to pick the right one for your production workload.
MCP is rapidly becoming the standard wiring layer between AI models and real-world tools. If you haven't integrated it into your stack yet, here's exactly what you need to build, connect, and ship before it becomes table stakes.
Large Language Models don't distribute attention evenly across a prompt. Researchers have discovered that certain early tokens can act as "attention sinks," attracting disproportionate attention and influencing outputs in unexpected ways. Learn how attention sink tokens work, why they affect model
AI agents can scaffold an entire feature in minutes, which feels like magic until it ships. Here's where vibe coding quietly falls apart in real production systems and what to do about it.
You're scaling an LLM-powered feature and the bill is climbing fast. Meta Llama 4 Scout and GPT-4o Mini are both pitched as budget-friendly options — but which one actually costs less when you're handling millions of tokens per day?
Claude 4 Opus is Anthropic's most capable model yet, but switching without testing is a gamble. Here's exactly what to benchmark, where it shines, and what might surprise you before you commit your production workload.