Topic and Scope Restrictions

AI models are trained to be helpful and comprehensive. Left unconstrained, they will often include context, background, alternatives, and caveats that you do not need for your specific use case.

Scope restrictions in prompts tell the model to stay within a defined boundary — a topic, a domain, a perspective, or a set of considerations. Without them, you often get answers that are broadly correct but too wide to be directly useful.

The most direct scope pattern is "Focus only on X":

• "Focus only on the security implications — do not discuss performance."
• "Cover only the onboarding process, not the full product feature set."
• "Discuss only open-source tools. Do not mention commercial products."

This pattern works best when X is a clearly defined, bounded category. Vague focus instructions ("focus on the important things") are too subjective to be useful.

Exclusion constraints remove specific topics you do not want covered:

• "Do not mention pricing or cost."
• "Avoid any references to the 2008 financial crisis."
• "Do not discuss the limitations of this approach."
• "Exclude any mention of political parties."

Exclusion constraints are useful when you know in advance that the model will naturally include something you want left out. They are especially important for brand-safe or compliance-sensitive content.

Domain-bounding constraints keep the model within a specific field or context:

• "Stay within the topic of mobile app development."
• "Limit your answer to the context of small retail businesses."
• "Keep this within the scope of Python 3.10 and later."
• "Your answers should only address the EU regulatory context."

Domain bounds are especially important when you are using the model in a vertical application (e.g., a cooking assistant that should not give medical advice).

Competitive exclusion is a common requirement for marketing and product content:

• "Do not name or compare to any competing products."
• "Focus on our product's strengths. Do not mention the competitive landscape."
• "Avoid any comparison to other CRM tools."

This scope restriction is important for keeping AI-generated content brand-safe. Models trained on internet data know about competitors and will mention them if not explicitly restricted.

You can restrict not just the topic but the perspective the model takes:

• "Present only the arguments in favor of remote work. Do not include counterarguments."
• "Analyze this from the perspective of a first-time homebuyer only."
• "Focus on the technical implementation. Do not discuss business or strategy considerations."

Perspective restrictions help when you need partial analysis for a specific purpose — a debate preparation brief, a stakeholder-specific report, or a one-sided persuasive piece.

For applications with persistent scope limits, encode them in the system prompt. This prevents users from accidentally or deliberately pulling the model outside its intended domain:

Scope restrictions do not always hold perfectly. Common failure modes:

• Drift — Model stays in scope for the first part of the response then drifts out
• Implicit inclusion — You restricted explicit mentions but the model implies the restricted content
• Ambiguous boundaries — Your scope definition was not precise enough for the model to apply consistently

The fix for all three: make scope boundaries more explicit and specific. Instead of "stay on topic", list exactly what is in scope and what is out of scope as two distinct lists.

You can frame scope as inclusions (what to include) or exclusions (what to leave out). Both are useful and they work together:

Positive (inclusion): "Cover only: feature descriptions, pricing tiers, and integration options."

Negative (exclusion): "Do not include: company history, team bios, or technical architecture details."

For complex scope requirements, use both: define the in-scope list explicitly AND the out-of-scope list. This double boundary is more robust than using either framing alone.

Temporal scope restrictions bound the time period the model should focus on:

• "Only discuss events from 2020 to the present."
• "Focus on the current state of the technology, not its historical development."
• "Ignore any developments after 2022 — our audience is not aware of them yet."

These are useful when writing for audiences with limited background, or when historical context would distract from a practical present-focused answer. Note: models may have knowledge cutoff limitations that affect accuracy for very recent events.

Scope can also control the level of granularity or depth:

• "Give a high-level overview — do not go into technical implementation details."
• "Focus on implementation specifics. Skip the conceptual background."
• "Stay at the strategic level. Tactical details are out of scope for this document."

Granularity scope restrictions are particularly useful when you are creating layered documentation — one version for executives, one for engineers — and need each to stay at its appropriate level without bleeding into the other.

You are writing a prompt for a cooking assistant application. The assistant should help with recipes, ingredients, cooking techniques, and kitchen equipment. It should NOT give nutritional advice, medical dietary guidance, food allergy warnings, or discuss non-food topics.

Which system prompt best encodes this scope?

Scope restrictions focus AI output by defining what is in and what is out. The core patterns are: focus only on X, do not mention Y, stay within domain Z, and perspective bounding.

The most robust scope encoding combines positive and negative framing, uses explicit lists rather than vague terms, and places the restrictions in the system prompt for persistent applications.

In the next lesson, you will apply constraints at the style level — controlling not just what the model says but how it says it.