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.

What is the Model-Specific Prompt Formatter?

Takes a generic prompt and outputs reformatted versions for GPT-4, Claude, Gemini, and Llama, each following that model's documented best-practice structure such as system and user split, XML tags, instruction prefixes, and chat templates. It runs free in your browser on Gera Tools, with nothing uploaded.

Model-Specific Prompt Formatter

Name: Model-Specific Prompt Formatter
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

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.

What each model’s preferred format looks like

GPT-4 (OpenAI messages API): The API takes an array of {role, content} objects. Instructions belong in the system role — models like GPT-4 follow a strong system message reliably. The user turn contains the actual task content.

[
  { "role": "system", "content": "You are a precise editor..." },
  { "role": "user",   "content": "Edit the following paragraph for clarity:..." }
]

Claude (Anthropic API): Claude responds well to < > XML-style tags that separate distinct sections of a prompt. A system parameter holds general instructions, and the user turn wraps content in named tags.

system: You are a precise editor. Focus on clarity and brevity.

user: <document>
[paste text here]
</document>

Edit the document above for clarity.

Gemini (Google Generative AI): Gemini works well with an explicit instruction prefix at the top of the prompt, followed by a clearly labeled task section.

INSTRUCTION: You are a precise editor. Focus on clarity and brevity.
TASK: Edit the following paragraph for clarity:
[paste text here]

Llama (direct inference): Open Llama chat models expect special role tokens wrapping each turn. These are added by the tokenizer when using a hosted API, but must be added manually when calling the model directly.

[INST] <<SYS>>
You are a precise editor.
<</SYS>>

Edit the following paragraph for clarity: [paste text here] [/INST]

When formatting differences actually change results

Formatting matters most for longer, structured prompts where the model must juggle instructions, reference material, and a task. A short single-sentence prompt behaves similarly across models regardless of wrapping. But when your prompt includes a long document to analyze, multiple constraints, or a specific output schema, the structure signals to the model what is context versus instruction versus task — and models vary in how reliably they pick that up without explicit structure.

Tips

XML tags help Claude most when your prompt mixes instructions with longer reference content — they prevent the two from blurring.
Llama tokens matter only on direct inference calls. Hosted chat UIs add them automatically, so the plain or GPT-style version works there.
Keep your base prompt structureless — write instructions then task in plain prose and let the formatter add the scaffolding. Adding your own structural markup before formatting can conflict with the output.
Compare outputs side by side on the same task to see which model’s response best matches your quality bar.