Route Time-Sensitive Questions to Web Search

The core idea is simple: combine retrieval with live web search when freshness matters, so your RAG system can answer time-sensitive questions with citations and current public evidence.

Problem framing

If every question goes to the vector store, time-sensitive answers go stale. If every question goes to web search, the system becomes slower and noisier than it needs to be.

The answer is a router.

A router is a small decision layer that checks the question and chooses the retrieval path. Prismfy is the live search path. Your retriever is the stable path. The router decides which one should handle the request first.

Why this matters now

Routing is where many RAG systems fail in practice. The code works, but the question type is not being classified correctly.

The high-risk cases are easy to spot:

• pricing and plan questions
• release or launch questions
• policy and compliance questions
• docs pages that update frequently
• comparisons involving current public sites

Those should almost never be answered from an old index alone.

Step-by-step solution

Use a lightweight routing checklist:

1. Does the question depend on current public information?
2. Is the answer likely to change within days or weeks?
3. Is there a single domain that should be trusted first?
4. Would a stale answer create user confusion or product risk?
5. If any answer is yes, route to Prismfy first.

You can implement that with heuristics, a classifier, or a small LLM prompt. The important thing is that the router is explicit.

Code example

This TypeScript example routes time-sensitive questions to Prismfy and leaves stable questions to your existing retrieval layer.

function isTimeSensitive(question: string): boolean {
  const signals = [
    "pricing",
    "price",
    "today",
    "current",
    "recent",
    "launch",
    "release",
    "policy",
    "docs",
  ]

  const lower = question.toLowerCase()
  return signals.some((term) => lower.includes(term))
}

async function liveSearch(query: string) {
  const response = await fetch("https://api.prismfy.io/v1/search", {
    method: "POST",
    headers: {
      Authorization: `Bearer ${process.env.PRISMFY_API_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      query,
      timeRange: "week",
      language: "en",
      page: 1,
    }),
  })

  if (!response.ok) throw new Error(`Search failed: ${response.status}`)
  return response.json()
}

async function handleQuestion(question: string) {
  if (isTimeSensitive(question)) {
    return liveSearch(question)
  }

  return { mode: "retriever", query: question }
}

That router is intentionally basic. In production you can add domain matching, source confidence, and fallback logic. But the first version should just separate stable questions from questions that depend on recency.

Practical notes and caveats

Do not overfit the router to keywords alone. “Best practices” may or may not be time-sensitive depending on the subject. “Pricing” almost always is. Domain-specific rules matter.

It also helps to log the route decision. If a user complains about a stale answer, you need to know whether the system sent the question to the wrong path or whether the live search results were weak.

When the live search results are thin, return a smaller answer with a note rather than falling back to an outdated retriever result without telling the user.

Why Prismfy fits this workflow

Prismfy fits because it gives the router a visible, synchronous live search branch. That makes it easy to trace why a question was answered from current public evidence rather than the index.

The router stays small. The answer system stays understandable. And the user gets the right source for the question type.

FAQ

How do you know a RAG answer needs live search?

Usually the query mentions dates, releases, pricing, changes, or current public facts. Those are strong signals to route the answer through a freshness-aware workflow instead of relying on old indexed chunks alone.

Does live search replace vector search?

No. The better pattern is hybrid: use retrieval for stable knowledge and use web search when the answer depends on freshness, citations, or current public evidence.

Related Prismfy guides

• How to Add Staleness Checks to a RAG Pipeline with Live Web Search
• Freshness vs Recall in RAG: When Web Search Beats Vector Search
• Add Real-Time Web Search to an OpenAI Agent Workflow with Prismfy

Try Prismfy

Create a Prismfy key, test POST /v1/search, and wire the search step into the workflow you care about first.