Grok Prompt Library: Templates for xAI's Grok (2026)

Grok is xAI's family of chat models. You can use it through xAI's offerings at x.ai and build on it through the API at docs.x.ai.

Before relying on any specific feature — current model names and versions, context limits, pricing, or any real-time or X-platform grounding — check xAI's official docs. Those details move quickly, and this page intentionally does not assert version numbers or limits that we can't point you to a live source for.

A practical rule: if a task depends on up-to-the-minute information, don't assume the model has it. Either provide the data in your prompt, or confirm in docs.x.ai whether the feature you need is available and how to enable it. When in doubt, ask the model to tell you what it cannot verify.

This skeleton works across capable chat models, Grok included. Fill the brackets and trim what you don't need.

``` You are a [ROLE] with [RELEVANT EXPERTISE]. Task: [ONE-SENTENCE GOAL]. Context: - Audience: [WHO THIS IS FOR] - Constraints: [LENGTH, TONE, MUST-INCLUDE, MUST-AVOID] - Inputs: [PASTE ANY DATA OR SOURCE TEXT HERE] Output format: [EXACT STRUCTURE YOU WANT]. If you are unsure or can't verify something, say so explicitly. ```

The last line matters more with Grok than with most models if you're tempted to lean on real-time knowledge: it gives the model permission to flag uncertainty instead of guessing.

``` Explain [TOPIC] to a [AUDIENCE] in [N] sentences. Use plain language, no jargon. End with one concrete example. If any part is contested or uncertain, note it. ```

``` Summarize the text below into: 1. A one-line takeaway. 2. Three key points. Use only what's in the text. Don't add outside facts. TEXT: [PASTE] ```

``` Draft a [FORMAT — e.g. post, email, outline] about [TOPIC] for [AUDIENCE]. Tone: [e.g. direct, lightly witty]. Length: [e.g. under 120 words]. Give me 2 variations. ```

``` Rewrite the text below to be [clearer / shorter / more formal]. Keep the meaning identical. Don't invent new claims. TEXT: [PASTE] ```

Extraction tasks are where explicit fallbacks pay off — they stop the model from filling gaps with guesses.

``` From the text below, extract a JSON object with these keys: { "name": ..., "date": ..., "amount": ..., "status": ... } If a value isn't present in the text, use null. Do not infer. TEXT: [PASTE] ```

``` Solve this step by step, then state your final answer on its own line prefixed with "Answer:". After the answer, list any assumptions you made. Problem: [PASTE] ```

Asking for assumptions surfaces where the model filled a gap — useful any time the inputs are incomplete.