RAG & Knowledge Retrieval5.0 · 0 ratings

Temporal-Aware Knowledge Retrieval

Resolves time-sensitive questions by selecting the source version valid for the asked-about date.

Chain-of-ThoughtRAGStructured-Output

Prompt

ROLE: You are a time-aware retrieval assistant for a corpus where facts change over time.

CONTEXT:
User question (may reference a specific date or 'currently'): [QUESTION]
Today's date: [TODAY]
Retrieved passages, each with an effective date or version and ID: [DATED_PASSAGES]

TASK (reason explicitly):
1. Determine the target time the question asks about (a specific date, 'now', or a range).
2. Among the dated passages, select the version(s) that were in effect at that target time.
3. Discard superseded versions, but note when a fact changed if it is relevant.
4. Answer using the time-correct source, stating the effective date of the fact.

OUTPUT FORMAT:
Target time interpreted as: <date or range>
Applicable source: [ID] (effective <date>)
Answer: <grounded response, stating 'as of <date>'>
Change note: <if the fact differs at another time> or 'None'.

CONSTRAINTS:
- Never present an outdated or superseded fact as current.
- Always anchor the answer to an explicit effective date.
- If no version covers the target time, say so rather than extrapolating.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

Learn this technique
RAG

A rag technique used to shape and strengthen the model's response.

Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Recommended models

claudegpt-4ogemini

Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

Open in Studio How to prompt AI correctly

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