Fabric

Two system prompt additions together mitigate this. Apply both. If you need to limit prompt length, use only the first, which keeps most of the effect. The first tells the model not to ask about work already requested and to carry out the next steps it has stated:

Critically Evaluating LLMs: What Data Visualization Can Teach Us

Some problems were reframed as issues with the workplace (devaluing of labor and expertise, understaffing and underfunding), not inherent issues with how AI technology works.

However, the reduction in nuance and idea building is a consistent blocker to implementations that meet our standards for non-AI work in data visualization.

Space to openly acknowledge the significant failures of AI tools not only validated lots of experiences in the room, but it also made the genuine use cases for AI tools feel more rewarding to explore.

“what if the slow parts about prototyping are actually what make it worth doing?”

The key point here is that prototyping is where we test the intellectual rigor of an idea

You have to be in the driver’s seat, because the tool is a reflection of what you ask for paired with a repackaged, statistically likely output of what others have already said on the topic. Elavsky puts it so well: “[P]eople tend to assume that the ideas they have in their head are really good, if they aren’t used to rigorously iterating on ideas.”

As Elavsky helps us to see, being thoughtful about when in the process we use AI helps us to develop robust ideas that are worthy of robust technical ends.

Accuracy: Is it true and verified? Security: Is my data safe and private? Sovereignty: Am I using the tool the way I want to?

Announced at Build, Fabric Apps introduce a new AI-first way to build custom web apps with Microsoft Fabric as the backend. For analytics teams, this means developers and their AI coding agents now have an accelerated path to building enterprise-grade data apps directly on their semantic models. Organizations can create and deploy operational data apps that leverage the same trusted business logic and the same governance as the rest of their analytics stack. From financial planning to inventory management to pricing optimization, or really any app you can describe, your coding agent can build a custom-tailored app based on your specifications and semantic model in just a few prompts. And it's not just about polished UI. Coding agents can trivialize features that are used to take real engineering effort, like persona-specific views, custom calendar interfaces, bespoke business logic, and more.

The fix is fewer, more heavily governed logical models: curate a small set of canonical, single source-of-truth datasets that are clearly owned, consumption-ready, and discoverable, then aggressively deprecate the near-duplicates.

The fix is fewer, more heavily governed logical models: curate a small set of canonical, single source-of-truth datasets that are clearly owned, consumption-ready, and discoverable, then aggressively deprecate the near-duplicates. Physical rollups and caches still matter for cost and performance

The goal is that when an agent searches for a concept, it finds a single governed answer.

Create pairwise skills: a knowledge skill acts as a thin top-level router that allows additional domain details to load on demand

The unbook skill encodes the process a senior analyst would follow: clarify the question, find sources (via the knowledge skill), run the query, and then loop the result through adversarial review sub-agents. It also bundles a dozen reusable analysis patterns (retention curves, rate decomposition, funnel analysis) so that common requests don't get reinvented each time.

Treat skill maintenance as a first class citizen

Dashboard-based evals are auto-generated by Claude (then human validated), covering the most common stakeholder questions

A domain owner can't announce the agent to their stakeholders until their slice of the eval set clears some threshold

We then verified in transcripts that it actually read them before every answer. Accuracy moved by less than a point in either direction

Two of ours: stacking additional rounds of doc refinement past a certain point (we hit three consecutive net-negative iterations: the docs were getting longer, not better), and swapping the adversarial reviewer to a cheaper model to cut latency (it lost most of the accuracy wins, for no real speedup

Claude skill to aggressively challenge all underlying assumptions on a potential final answer increased accuracy by 6% within our eval set, but at the cost of 32% more tokens and 72% higher latency.

every response carries a footer that contains which source tier it came from

We often see companies building a significant amount of infrastructure to account for current model shortfalls that become moot once those models improve

Some of the processes we discussed may be overkill if, for example, you don’t produce much data, you only have a few consumers of the output, or your data model is likely to remain simple.

How technical is the intended audience of the output?

poor or stale documentation. Claude is exceptionally useful for closing the gap (drafting column descriptions, proposing metric docs from query patterns, flagging undocumented models in CI), but the curation and ownership are managed by humans.

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