Do these formatting differences actually change results?

They can. Claude responds well to XML-tagged structure and a clear system role, GPT-4 to a clean system and user split, Gemini to an explicit instruction prefix, and Llama to its chat template tokens. Matching the convention reduces parsing ambiguity and tends to improve adherence.

Does the formatter change my wording?

No. It only restructures — adding the system or user split, XML tags, prefixes, or template tokens around your text. Your instructions and content are preserved verbatim, so meaning never shifts.

What is the Llama chat template?

Open Llama chat models expect special tokens that mark the system, user, and assistant turns. The Llama version wraps your prompt in those tokens, which matters when you call the model directly rather than through a hosted chat UI that adds them for you.

Can I trust one format to work everywhere?

A plain prompt works on every model, but each provider documents a preferred structure. The point of this tool is to give you the provider-preferred version for each, so you are not hand-converting between four conventions.

Model-Specific Prompt Formatter

Model-specific prompt formatter

A prompt that works well on one model is rarely formatted the way another model expects. Claude leans on XML tags and a strong system role; GPT-4 wants a clean system and user separation; Gemini does best with an explicit instruction prefix; and open Llama models need chat-template tokens when called directly. This formatter takes one base prompt and produces the provider-preferred version for each, so you stop hand-converting between four conventions.

How it works

You write a single plain prompt. The tool then wraps it in each target model’s documented structure without touching your wording. For GPT-4 it splits your text into a system message (the instructions) and a user message (the task). For Claude it builds a system block and wraps the task content in XML tags the model reads well. For Gemini it adds an instruction prefix and a clear task section. For Llama it inserts the chat-template tokens that mark each turn. Every version is shown ready to paste into the matching playground or API call.

Tips and notes

The XML tags help Claude most when your prompt mixes instructions with longer reference content — tags keep the two from blurring.
Llama tokens matter only on direct calls. Hosted chat UIs add them for you, so use the plain or GPT-style version there.
Keep your base prompt structureless. Write instructions then task in plain prose; let the formatter add the per-model scaffolding.
Compare outputs side by side. Running the same intent through all four is the fastest way to see which model suits your task.