Stop trying to pick the one best AI model
Model leaderboards change monthly. The teams getting the most from AI stopped betting on a single vendor and started routing each task to the model that fits it.
Blog
Notes on working with AI at work.
Opinions, engineering lessons, and practical habits from the team behind ChatLite. For the evergreen definitions and how-tos, see Documentation.
Latest
10 articles- Opinion
- Engineering
What running AI guardrails in production taught us
Screening every prompt and response sounds simple until real traffic arrives. Lessons from operating guardrails at scale: latency budgets, false positives, and what to log.
- Best practices
Five prompt habits that make teams faster
The gap between an average AI user and a great one is habits, not talent. Five practices worth standardising across a team, with examples.
- Product
When a question deserves deep research, not a chat reply
A chat answer is a first draft from memory. Deep research plans a search path, reads live and uploaded sources, and returns a cited brief. Knowing which to reach for saves hours.
- Opinion
The real cost of AI tool sprawl
Five AI subscriptions, five data-handling policies, zero shared context. Tool sprawl is quietly expensive, and the bill is not just the invoices.
- Engineering
What we learned helping teams self-host AI
Self-hosting an AI workspace is very doable, but the hard parts are rarely the ones teams plan for. Notes from real deployments: networking, model access, and upgrades.
- Best practices
Your AI is only as good as the data it can see
Generic answers come from generic context. How grounding AI in live company data changes the quality of every response, and how to do it without opening a security hole.
- Product
AI image generation at work: beyond the novelty
Past the demo phase, image generation earns its keep in specific, repeatable jobs: product mockups, internal comms, and design exploration. Where it fits and where it doesn't.
- Adoption
Shadow AI is already in your company. Now what?
Employees adopted AI before IT approved it. Banning it fails quietly; a sanctioned, governed alternative wins by being genuinely better than the workaround.
- Adoption
You rolled out AI. How do you know it's working?
Seat counts flatter and screenshots lie. The adoption metrics that actually predict value: repeat usage, task coverage, and where AI output ends up.