Agents turn simple keyword search into compelling search experiences
3 hours ago
- #Search Optimization
- #AI Agents
- #RAG
- Traditional RAG systems resemble conventional search APIs with added query understanding and reranking, optimized for user engagement.
- Agents in RAG systems can reason and adjust queries dynamically, building a 'mental model' of the search tool's behavior.
- Complex search APIs may hinder agent reasoning; simpler, predictable search backends can be more effective.
- A simplified keyword search approach, explicitly explained to the agent, can yield surprisingly effective results.
- Agents can evaluate and remember the effectiveness of past searches, improving future query adjustments.
- Semantic caching allows agents to recall and leverage past successful queries for similar searches.
- The agent's ability to judge search results is crucial and depends on metadata quality and domain expertise.
- User clickstream data, which captures actual user behavior, is often missing in RAG systems, leading to a gap in understanding user preferences.
- Agent reasoning may conflict with user engagement data, as agents prioritize explicit reasoning over implicit user behavior patterns.
- Optimizing for user engagement might require traditional search stack approaches, separate from agent reasoning.