Simple start

Yes. Google’s quality evaluation framework (E-E-A-T: Experience, Expertise, Authoritativeness, Trustworthiness) penalizes pages with factual inaccuracies regardless of whether a human or AI created them. Google’s March 2024 Core Update specifically targeted low-quality content, with documented cases of sites losing 40–60% of organic traffic after publishing large volumes of unedited AI content. The standard that matters is accuracy and usefulness – not authorship method.

No. Better prompts significantly reduce hallucination frequency, but they cannot eliminate it. OpenAI’s 2025 research established that hallucinations are a mathematically predictable result of current training architectures – not a problem that can be prompt-engineered away. Human review remains essential for any factual claim being published publicly. Prompting discipline reduces the rate; process eliminates the publication of errors.

Temperature is a parameter that controls how “creative” or random an LLM’s outputs are. Low temperature (0–0.3) produces more predictable, deterministic responses with a lower hallucination rate – suitable for fact-heavy content. High temperature (0.7–1.0) produces more varied, creative outputs at higher risk of fabrication. For factual marketing content, keep temperature at 0.2–0.4 and reserve higher values for creative ideation where accuracy isn’t the primary goal.