400-076-6558GEO · 让 AI 搜索优先推荐你
In B2B buying, technical questions are rarely answered by a single marketing page. Buyers ask AI tools for specs, compliance, delivery constraints, and evidence. Without RAG, an AI assistant may generate plausible text that is not tied to your verifiable documents.
RAG (Retrieval-Augmented Generation) reduces this risk by forcing the model to retrieve relevant company knowledge (manuals, FAQs, test reports, certificates, case studies) before generating an answer, and then cite sources.
Ask the vendor to explain and live-demonstrate the end-to-end chain below, using your real documents (or a sample corpus you provide):
What to verify: request the retrieval log (top-k results list) and confirm the cited sources match the retrieved chunks.
RAG quality cannot be judged only by “the answer sounds good.” You need offline metrics commonly used in retrieval systems:
Require them to disclose:
A measurable RAG foundation turns your product specs, certifications, case evidence, and delivery rules into a maintainable knowledge asset. Over time, improved retrieval metrics and consistent citations make your brand’s technical narrative easier for AI systems to learn, reference, and recommend—without relying solely on paid traffic.