How do I reduce hallucinations when using search-grounded LLM responses?
Reduce LLM hallucinations by grounding responses in real-time search results with required citations. Query a web search API, extract clean content, and inject it into LLM context with instructions to cite sources. Require citations for every claim to create a verifiable chain from search result to output.
Hallucinations occur when LLMs generate plausible but false information. Search grounding mitigates hallucinations by providing verifiable web content the LLM must reference instead of relying on training data.
Effective grounding strategies
Effective search grounding requires multiple complementary strategies:
- Search before generation: Retrieve current information instead of relying on training data
- Provide multiple sources: Enable cross-verification by including content from several pages
- Use structured extraction: Apply structured extraction for factual data like prices, dates, and statistics
- Validate programmatically: Compare LLM outputs against source content to catch errors
Structured extraction reduces ambiguity for factual data. LLMs parsing free text are more prone to errors than when extracting into defined schemas with type checking.
Search quality and source selection
Search quality directly impacts grounding effectiveness. Poor search results lead to grounded but incorrect answers.
Use ranked results from authoritative sources to improve accuracy. Firecrawl's search API returns ranked results optimized for factual content. Apply recency filters for time-sensitive queries to ensure information currency.
Leverage search operators for precision. Domain restrictions focus results on authoritative sources. Date filters ensure currency. Better search inputs produce better grounding material.
Validation and verification techniques
Implement validation to catch hallucinations before outputs reach users. Compare LLM claims against source content programmatically. Flag responses without citations for human review.
Multi-step verification works for critical applications: search, extract, generate, validate. The validation step checks if claims match source content before returning responses to users.
Use confidence scores to weight responses based on source quality and consistency across results. Higher confidence indicates better grounding and lower hallucination risk.
Key Takeaways
Reduce hallucinations by grounding LLM responses in fresh search results with required citations. Validate outputs against sources programmatically. Use structured extraction for factual data and prioritize high-quality, ranked results from authoritative sources.
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