What is LLM grounding, and how does it work with a search API?

LLM grounding connects language models to verified external data, such as private documents, databases, knowledge graphs, or live web sources, to improve accuracy, currency, and traceability while reducing unsupported responses. It generally involves retrieving relevant evidence, placing concise passages into the model’s context window, and attributing claims to sources through citations. Common approaches include retrieval-augmented generation for controlled document collections, fine-tuning for domain-specific behavior, knowledge graphs for precise entity relationships, and tool or API calls for current external information. Using web search APIs can support answers about frequently changing public information by returning source-linked passages, while effective retrieval depends on accuracy, precision, and efficient use of context tokens. The discussion also notes that grounding does not eliminate hallucinations and requires careful evaluation, particularly in regulated sectors where providers’ data retention, security audits, and compliance controls are important; Exa presents its Search API and Enterprise controls as options for these use cases.

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