You are building a RAG-based support assistant in Azure AI Foundry. Users ask follow-up questions like "What about the enterprise tier?" after previously asking "Does the standard plan include SSO?" Retrieval quality drops sharply on these follow-ups because the vector search embeds only the literal follow-up text, which lacks the subject and intent from earlier turns. Which technique should you add to the retrieval pipeline to fix this?