Google Cloud's hallucination mitigation guide covers the practical fixes most users can actually apply

IBM's hallucination explainer covers causes and general principles. Google Cloud's guide https://cloud.google.com/discover/what-are-ai-hallucinations is more practically oriented toward what you can actually do to reduce hallucinations in real workflows and products.

The mitigation techniques the guide covers are worth understanding as a spectrum from most to least accessible. Using relevant, authoritative sources, which is essentially the RAG approach, is the highest-impact mitigation for factual queries and is available to anyone who can structure a retrieval pipeline or paste relevant documents into context. Limiting possible outputs through structured prompting and output formatting is the next level, available to anyone who writes prompts deliberately. Templates, feedback loops and training data improvements sit at the infrastructure level that requires more engineering investment.

The grounding emphasis being central to the guide is the alignment with current best practice: models that are constrained to answer from specific retrieved context make different types of errors from models answering from training data alone. The errors are more predictable and more detectable.

Pairing this with the IBM hallucination post (linked in the first evergreen thread) gives a complete picture of the problem and the mitigation landscape.

Which hallucination mitigation technique do you think is most realistic for everyday AI users who do not have access to an engineering team to implement RAG or fine-tuning?