Several crucial guardrails prevent wrong details in agent-written spec docs. Foremost is grounding the AI with accurate, pre-vetted source material, often through Retrieval-Augmented Generation (RAG), ensuring it pulls facts from trusted databases rather than hallucinating. Strict prompt engineering provides clear instructions and contextual boundaries, guiding the agent to focus on relevant information and reducing ambiguity. Structured templates and schema enforcement further reduce errors by dictating expected content types and formats, minimizing speculative or off-topic details. Implementing iterative feedback loops allows the AI to learn from corrections, continuously improving accuracy. Ultimately, rigorous human review and validation remain indispensable, acting as the final checkpoint to catch any subtle inaccuracies or misinterpretations before documentation is finalized. This combination of data-driven grounding, precise prompting, structural guidance, and human oversight forms a robust defense against erroneous information. More details: https://tilo-hammer.com/bitrix/redirect.php?goto=https://infoguide.com.ua