What Constraints Do
Constraints are negative instructions — they tell the model what to avoid. This is crucial because models have strong defaults: they tend to be verbose, use clichés, add disclaimers, and hedge. Constraints override these defaults. Think of them as the “don’t” list you’d give a new team member.
# Adding constraints:
"Constraints:
- No buzzwords (don't use 'synergy',
'leverage', 'streamline', 'empower')
- No generic claims ('powerful', 'robust',
'cutting-edge', 'world-class')
- Total length: under 150 words
- Tone: direct and confident, not salesy
- Don't mention competitors by name"
# The output is now tight, specific,
# and free of the generic filler that
# plagues most AI-generated copy.
Constraint Patterns
# Constraints that fix common problems:
# Too verbose:
"Max 100 words"
"One paragraph only"
"Be concise — every word must earn its place"
# Too generic:
"Use specific numbers, not vague claims"
"No adjectives without evidence"
# Too hedgy:
"No disclaimers or caveats"
"Don't say 'it depends' — commit to an answer"
# Wrong audience:
"Assume the reader is technical"
"Don't explain basic concepts"
# Hallucination risk:
"Only use information I've provided"
"If unsure, say 'I don't know'"
Key insight: Constraints are often more powerful than positive instructions. Telling the model what NOT to do is sometimes easier than describing exactly what you want. “Don’t use buzzwords” is clearer than “use authentic language.”