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Qualitative constraint language

As explained in Prompt Engineering in Practice

Qualitative safety language consists of vague prompt directives that express desired safe behavior without specifying precise conditions and requirements.

Qualitative safety language uses directives such as “use your judgment,” “be responsible,” or “avoid harmful outputs” without specifying predictable behavior. Because the model must interpret terms like “harmful” in context, its interpretation can drift under roleplay, legitimacy framing, or gradual escalation. section 10.3.2

Replace vague intent with explicit conditions and responses:

  • Define scope positively: answer only questions about company-wide HR policies. For requests about individual employee records, prescribe an exact response. section 10.3.2
  • For a code review assistant, specify the review scope, rule out personas and off-topic tasks, and provide a fixed fallback for requests outside scope. section 10.3.1

Test for adversarial pressure

A user frames a request for individual employee records as urgent, managerial, and legitimate. Test whether the prompt enforces its privacy constraint in that case, not only with benign inputs. section 10.3.2

How Prompt Engineering in Practice covers it

How deep it goes
Contrasts vague instructions with explicit scope and fixed responses, then identifies adversarial conditions that can expose interpretive drift.
Examples
Uses an HR chatbot protecting employee records and a code review assistant with a fixed fallback.
What you can do after
Evaluate prompt safety by testing benign and adversarial inputs for gaps between intended and specified behavior.
How it connects
Connects system prompts with prompt constraints and adversarial testing.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Prompt Engineering in Practice. The section links point to the parts of the book it draws on.