Use-cases
Real-world applications at work
AI for code review: what it catches and misses
An AI reviewer is fast, tireless, and easy to add to a pull request. Here is what it reliably catches, where it quietly fails, and how to use it well.
AI for customer insights from reviews
Thousands of reviews, summarized into themes by AI. The promise is real, and so are the ways it quietly misleads. Here is the honest version.
Translation with LLMs: where it shines and fails
Language models translate fluently enough to feel solved. Here is where they genuinely shine, where they quietly fail, and why fluency hides the errors.
Content moderation with AI: the hard tradeoffs
AI moderation scales to volumes humans never could — but every dial you turn trades one harm for another. Here are the tradeoffs you cannot escape.
Personalization with AI without creeping people out
AI makes personalization cheap and precise — which is exactly why it can feel invasive. Here is how to be relevant without crossing the line.
Document Q&A that actually works: patterns and pitfalls
Asking questions over your own documents is the most useful AI demo and one of the easiest to get quietly wrong. Here are the patterns that survive real use.
AI coding for non-engineers: promise and limits
AI lets non-engineers build software they could never write by hand. Here is what that really unlocks, where it quietly breaks, and how to stay safe.
Meeting transcription and summaries: the honest version
Automatic meeting notes are the AI feature people actually want. Here is what works, what quietly breaks, and why the summary is the easy part.
Classifying and routing text at scale
Sorting and routing text by category is one of AI's most reliable jobs. Here is what makes it work at scale, and the failures that wait at the edges.
Data extraction with LLMs: turning messy text into tables
Turning unstructured text into clean rows and columns is where LLMs quietly shine — if you define the schema, validate every field, and plan for the messy inputs.
AI in education: tutor, not oracle
AI can be a patient, always-available tutor — or a homework-answering oracle that quietly erodes learning. The difference is in how you use it.
AI for writing: where it helps and where it hurts
AI is a fast first-drafter and a dangerous final editor. Here is where it lifts writing, where it quietly degrades it, and how to tell the difference.
Marketing copy with AI: the workflow that works
AI can draft marketing copy in seconds, which is exactly why so much of it is forgettable. Here is the workflow that turns speed into copy that works.
AI agents at work: realistic tasks vs demo theater
Agent demos are dazzling and agent deployments are humbling. Here is what actually works at work, what falls apart, and how to tell which is which.
AI search inside your company: the realistic version
Ask a question, get an answer from all your internal documents. The demo is magic. Here is what makes it hard once real data and real permissions arrive.
AI for research and literature review
AI can compress weeks of literature review into hours — and quietly invent citations that do not exist. Here is how to get the speed without the errors.
Putting an LLM in customer support: what breaks first
A support chatbot is the easiest AI demo and one of the hardest things to run well. Here is where real deployments break — and what separates the ones that survive.