As language models proliferate, evaluating them for specific use cases has become a critical and often underestimated challenge. Generic benchmarks tell you what a model can do;...

Healthcare has been one of the most hyped application domains for AI, and also one of the most challenging. After several years of real deployment, the picture is clearer: some...

AI inference costs have dropped dramatically, but understanding where the money actually goes is more complex than it appears. Token counts, model choices, infrastructure...

As retrieval-augmented generation became standard practice, vector databases went from niche to essential infrastructure. The market has consolidated around a few serious...

Hallucinations - confident, coherent outputs that are factually wrong - remain the central reliability problem for LLM applications. Two years of production experience has...

Retrieval-augmented generation has a deceptively simple core idea and a surprisingly complex production implementation. Walking through the full stack: document parsing,...

Developer productivity tools powered by AI code generation have been in widespread use for two years. The initial hype was substantial; the measured reality is more nuanced. A...

Two years ago, the best open-source language models were clearly behind the frontier closed models. The gap has closed substantially. Understanding which open models are...

Two of the most discussed strategies for adapting language models to specific tasks - fine-tuning and retrieval-augmented generation - each come with distinct tradeoffs in cost,...

The race to expand context windows has produced models that can accept millions of tokens in a single prompt. But raw context size does not tell you whether your application...

Autonomous AI agents - systems that plan, use tools, and execute multi-step tasks without continuous human input - have moved from demos to production deployments. The gap...

As models become better at following instructions and understanding intent, the role of manual prompt engineering has shifted. The best practitioners in 2026 are not writing...

On-device language models - running directly on phones, laptops, and embedded hardware without cloud calls - have crossed the threshold from experimental to practical. The...

The marketing claims for AI coding tools are extravagant. The reality, based on usage data from teams that have deployed them at scale, is more nuanced but still significant....

The conventional wisdom that machine learning requires enormous training datasets is outdated. Zero-shot and few-shot capabilities have fundamentally changed the data...

Running a 70B parameter model is expensive. Distillation transfers the capability of large models into smaller, faster, cheaper ones. Here is the current state of the technology...

Voice AI spent years as a parlor trick. In 2026, the technology has crossed a threshold where it handles real customer interactions. Here is what changed and where the remaining...

Real data is expensive, biased, and legally complex. Synthetic data sidesteps all of these problems. Here is how to generate it well, when it works, and where it leads models...

Prompt injection is the most common LLM security vulnerability — and most applications are not adequately defended against it. Here is how it works and what you can do.

DevOps workflows are absorbing AI capabilities faster than most engineering domains. Here is where the real productivity gains are in 2026, and where the hype still outruns the...