**Deterministic does not mean truthful.** With temperature 0 or greedy decoding, an AI model may produce the same answer every time. That makes the output repeatable—but not automatically correct. A model can be highly confident while working with incomplete information. The safer approach is to give it clear boundaries: 1. Ground the answer in verified context. 2. Provide an explicit refusal path: “Verification failed — the available data is insufficient.” 3. Check every important claim before presenting the final answer. 4. Never treat confidence as evidence. At HUQAN, we see determinism as a reproducibility feature—not a guarantee against hallucination. Trust needs evidence, boundaries, and a safe way to say “I don’t know.” **What safeguards do you use to keep deterministic AI systems from confidently guessing?** #AISafety #AIAgents #PromptEngineering #LLM #OpenSource #HUQAN