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The conventional wisdom that machine learning requires enormous training datasets is outdated.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...

Benchmarks tell you how a model performs in general. They do not tell you how it performs on your task. Here is how to build an evaluation framework that actually answers the...

AI writing tools have become standard in knowledge work. Here is an honest assessment of where they genuinely improve output quality, where they create false confidence, and how...

Search has been the same basic paradigm for 30 years. AI is finally changing it — not just by summarizing results but by reasoning over them. Here is what that shift looks like...

AI-powered code review tools are no longer novelties — they catch real bugs, enforce standards, and surface security issues before code ships. Here is how teams are using them...

The move from text-only to multimodal AI changes what you can build. Here is a practical look at what multimodal capabilities exist today, where they are strong, and where they...

Shipping an LLM application without observability is flying blind. Here is what good AI observability looks like in 2026, which tools provide it, and what metrics actually matter.

Getting reliable structured data out of language models used to require prompt tricks and fragile parsing. In 2026, structured output is a first-class capability — but using it...

AI safety is not just a research problem — it is a product engineering problem. Here is a practical approach to building guardrails, handling misuse, and shipping responsibly...

Each approach to getting better outputs from LLMs has different costs, latencies, and use cases. Here is a practical decision guide for engineering teams building AI products.

From Llama to Mistral to specialized fine-tunes, open source LLMs have closed much of the capability gap with GPT-4 and Claude. Here is the current landscape and when to use each.

Deploying an LLM is the easy part. Keeping it healthy, cost-efficient, and continuously improving in production is where the real work begins. Here is what LLMOps looks like in...