RAG systems need good retrieval before they can generate useful answers. Most examples focus on vector databases, embeddings, and chunking. Those are important, but there is another layer that often gets overlooked: How do you find fresh, relevant web sources in the first place? That is where a Search API can help. In an AI or RAG workflow, a Search API is usually used before crawling, embedding, or summarizing. It can provide structured search results for a user query, topic, company, product, or keyword. A typical flow looks like this: User query Search API Relevant URLs Fetch page content Clean and chunk text Embed or summarize Generate answer This is useful when the system needs information that changes often, such as: market research competitor monitoring product comparisons news discovery SEO research brand monitoring lead research topic discovery A Search API does not replace a vector database. It solves a different problem. The vector database helps retrieve from content you already collected.…