Grounding ensures AI outputs are anchored in retrieved facts rather than generated from potentially unreliable model knowledge. Problem: LLMs may generate plausible but false information from training data or hallucination. Grounding constrains outputs to verified sources. Mechanisms: Explicit grounding: Only answer from retrieved context, refuse if information not found. Soft grounding: Prefer retrieved info, mark uncertain claims. Verification: Check outputs against sources, flag unsupported statements. Implementation: System prompts emphasizing only using provided context, retrieval-augmented generation, post-generation verification against sources. Grounding indicators: Confidence scores, source citations, explicit uncertainty markers ("According to...", "The document states..."). Trade-offs: May refuse valid questions if retrieval fails, reduced creativity/synthesis. Enterprise use: Critical for compliance, legal liability, accurate customer support. Google's approach: Grounding API connects Gemini to Google Search for real-time factual grounding. Best practices: Clear grounding policies, handle "information not found" gracefully, combine with retrieval quality optimization. Foundation of trustworthy AI assistants.