A practical framework for giving AI coding and operations assistants the rules, knowledge, and real-time information they need to produce useful answers.
AI assistants' bottleneck is context, not model capability. Effective context requires Rules (team conventions), Knowledge (system runbooks), Retrieval (live operational data). For platform leads, context quality—not model choice—determines assistant reliability on incidents; stale documentation and missing escalation rules degrade output more than AI capability.
Most teams that adopt an AI coding or operations assistant eventually reach the same conclusion:
The model is rarely the bottleneck. The bottleneck is context.
A capable model with the wrong context produces a confident, generic answer. The same model with the right context can produce something an engineer can actually use.
So the practical question is not only:
Which model should we use?
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Every AI coding assistant promises the same thing: "Write code faster." Yet after months of using...
One of the most common questions I hear is: "Which LLM should we use?" It's an understandable...
Most AI coding workflows treat context as something that happens accidentally. You open a few files....
How I work with AI, and why the system around the model matters more than the model.
