What is real-time web search for LLMs?

Real-time web search for LLMs connects a language model to a live search index at inference time, supplying current information the model was not trained on. LLMs have a fixed training cutoff and have no knowledge of events, publications, or data changes after that date. Connecting a web search API to the model's tool-use or RAG pipeline provides up-to-date context as part of each generation request: the model issues a query, reads the results, and generates an answer grounded in live content rather than training data alone. This is distinct from fine-tuning, which updates model weights; search augmentation supplies context per query without changing the model.

Use real-time web search when the application needs to answer questions about recent events, current prices, newly published research, or any information that changes faster than model retraining cycles allow. For stable domain knowledge that does not change, a base LLM without search is simpler and faster. For RAG grounding over internal documents, a vector store is more appropriate than a public web search API.

Firecrawl's Search API provides structured, ranked results for any query issued at inference time. Combine it with the Scrape API to retrieve full page content: search surfaces the relevant URLs, scrape extracts clean Markdown, and the extracted text feeds directly into the LLM's context window.