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Design semantic views around recurring business questions and decisions—not around individual agents. Start with the terms people use and the answers they need, then model the business concepts and connect them to data. An agent can use that shared model, but it should not define the business domain or own a separate copy of metric logic.
Start with the questions people need to answer
Ask stakeholders, report owners, and analysts which questions recur and what decisions those answers inform. Capture the words they use, not just the names of tables or fields. Snowflake’s guidance puts the principle plainly: “Design from the perspective of your end users, not the database.” (Snowflake, Best practices for modeling semantic views.)
For each question, note who asks it, what decision it supports, and what they mean by its important terms. This makes the scope concrete: a view should serve a coherent business need, not merely mirror a source schema or one agent’s prompt format.
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Group concepts around bounded areas such as sales, marketing, or customer support. Separate views when users have distinct questions or need different interfaces—not simply because the underlying data is stored in different tables or schemas. Snowflake recommends organizing around business domains and use cases from the end user’s perspective (Best practices for modeling semantic views).
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A practical scope check is whether a business user can describe the view in ordinary language and recognize why its contents belong together. If the scope exists only because a particular agent needs it, reconsider whether it represents a durable business interface.
Model business concepts before mapping them to tables
Define the meaning and structure of the business model first. Snowflake’s getting-started guidance suggests working through four questions:
- What business entities exist in your data?
- How do these entities relate to each other?
- What metrics are important to your business?
- What dimensions do you use to analyze these metrics?
These are useful workshop prompts, not evidence that people commonly type these exact phrases into search engines. Once the concepts are clear, map them to source tables and views, specify joins, and define the calculations behind metrics. Snowflake recommends starting with a simple star schema as one possible approach, not as a rule for every model. See its Overview of semantic views.
Make the model understandable in business language
Descriptions and synonyms help users and consuming systems connect their wording to the modeled concepts. Use terms stakeholders actually apply, and explain what each entity, metric, relationship, and filter means. Make valid analysis paths explicit so that the model communicates not only what a concept is, but how it can be used. Snowflake’s modeling guidance covers descriptions, synonyms, relationships, metrics, filters, and testing as parts of semantic-view design (Best practices for modeling semantic views).
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Test with representative questions and revise
Run representative stakeholder questions against the model. Check whether the concepts selected, joins followed, and metric interpretations match the intended business definition. If a question produces an ambiguous or misleading interpretation, revise the definitions, relationships, descriptions, or scope, then test again. Treat this as an iterative modeling process, not a one-time translation of a database schema.
Keep semantic definitions reusable, with platform limits in view
A semantic model is most useful when its business definitions can serve more than one consumer, rather than being recreated separately for each agent or interface. Snowflake says semantic views can be queried directly and used with Cortex Agents (Overview of semantic views). dbt describes its Semantic Layer as defining metrics on existing models and handling joins (dbt Semantic Layer).
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Those are documented capabilities of particular platforms, not a guarantee that one model will work unchanged across every BI tool, SQL workflow, API, or AI system. Before choosing an implementation, check the consumers you need, how centrally defined metrics and joins are exposed, and how your team handles permissions, ownership, review, versioning, and change. The documentation cited here does not establish a neutral comparative benchmark that makes one approach universally better.
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Snowflake’s guidance notes that a large semantic view combined with agent instructions and conversation history can raise a context-window consideration at roughly 100,000 tokens. This is a product-specific modeling note, not a general measure of business effectiveness. Keep an agent’s usable context in mind when setting scope, but do not let the agent dictate the underlying business definitions (Best practices for modeling semantic views).
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