AI grounding

AI grounding adds a retrieval step before generation:

1. User prompt arrives.

2. Retriever fetches sources — search engines, vector databases, internal knowledge bases — that match the prompt.

3. Retrieved text is injected into the LLM's context alongside the prompt, with explicit instructions to cite and stay within the supplied evidence.

4. LLM generates the answer, weaving in citations back to the retrieved sources.

The result is an answer that can be traced back to a specific URL or document — not a confident-sounding paragraph the model invented from training memory.

Retrieval-Augmented Generation (RAG) is the most common implementation of grounding. Grounding is the broader goal — making sure the model's claims are tied to verifiable sources.

You can ground without RAG (by injecting curated context directly), and you can run RAG without strong grounding (if you don't enforce citation back to the retrieved text). In practice, mature systems use both: RAG to fetch evidence, plus grounding-enforcement prompts and citation validation to keep the model honest.

Grounding is the single biggest lever against hallucination. Frontier models still confidently generate plausible-sounding falsehoods on the long tail of factual queries — grounding cuts that error rate sharply for any prompt where retrieval is feasible.

For brands, grounding also exposes which sources AI engines trust. Perplexity, Google AI Overviews, and Bing Chat publish their citations — making it possible to measure (and influence) which pages get pulled into retrieval-grounded answers.

Five practices for production-grade grounding:

1. Retrieve first, generate second. Always fetch evidence before letting the model write — even for prompts you assume the model "knows."

2. Inject citations into context with explicit grounding instructions. "Answer using only the following sources. Cite the URL after each claim."

3. Validate citations at output time. Drop or flag any sentence whose claim is not supported by the retrieved text. Frontier models like GPT-4o and Claude can self-validate when prompted correctly.

4. Surface citation links to the user. Trust grows when users can verify the source. This is also how Perplexity, Gemini, and AI Overviews differentiate from training-only chat experiences.

5. Refresh the retrieval index continuously. Stale indices drag grounding back toward hallucination. Crawl freshness is a load-bearing requirement, not a nice-to-have.