Factual claims are bounded to deterministic scope

High temperature settings (≥0.8) combined with no factuality constraints in the system prompt create a confabulation-maximizing configuration. For research, knowledge, or information retrieval applications, this means the model will confidently generate plausible-but-invented statistics, misattributed quotes, and false technical specifications. OWASP LLM09 directly covers this failure mode. Temperature controls randomness in token sampling; at high values, the model increasingly selects lower-probability tokens — raising the rate of semantically coherent but factually incorrect output in a way that is invisible to users.

Lower the temperature to 0.2 or below for factual-domain applications and add an explicit factuality constraint:

const response = await openai.chat.completions.create({
  model: 'gpt-4o',
  temperature: 0.2,  // Was: 1.0
  messages: [
    {
      role: 'system',
      content: 'Answer factually and precisely. Do not speculate or extrapolate beyond what you know with confidence.'
    },
    { role: 'user', content: userMessage }
  ]
})

For code generation or creative tasks, higher temperatures are appropriate — apply the constraint selectively to routes that answer factual queries.