Why Agentic RAG can handle harder questions than standard RAG
Standard RAG turns a user question into a searchable form, pulls a few document chunks once, puts them into the LLM’s context, and then generates an answer. This can work well in demos, but it breaks down when a question has several parts or needs comparison across multiple sources. If the first search brings back weak or wrong evidence, the LLM may an answer that sounds confident but is not well supported.
changes the design from a one-pass pipeline into a loop. An agent plans what to search for, sends the question through the right path, checks whether the answer is grounded, and searches again if needed. The practical difference is between asking one database question and running a multi-step process.
The title claims this approach cut API latency by 45 milliseconds, but the provided text does not show the method or test setup behind that number.
Key points
- Standard es once, then answers from the retrieved chunks.
- It can fail when questions are multi-part or require comparing several sources.
- uses an agent to plan, route, check, and repeat the search process.
- The title says API latency dropped by 45 milliseconds, but the excerpt does not explain how it was measured.
- For token and cost control, should be used selectively rather than for every question.