Model-specific prompting: why you approach Claude, Gemini, and ChatGPT differently

Introduction: The myth of the universal prompt

In the early days of large language models, it was often assumed that a "good prompt" was a universally applicable recipe. Many users developed standard formulations, so-called megaprompts, and expected these to produce identical results in ChatGPT, Claude, and Google Gemini. In practice, however, this assumption turns out to be a persistent misconception. An instruction that produces fantastic, structured reports in Anthropic Claude can lead to an overly terse summary in OpenAI ChatGPT, and in Google Gemini to an unnecessarily narrative account that misses the factual core.

The difference in response behavior is not coincidence or arbitrariness. It is the direct result of fundamental differences in the underlying model architectures, the composition of the pre-training datasets, and above all the specific RLHF (Reinforcement Learning from Human Feedback) and DPO (Direct Preference Optimization) strategies of the developers. Each vendor sets different priorities when aligning the model with human preferences. Anthropic places heavy emphasis on reasoning steps, safety boundaries, and adherence to intricate XML structures. OpenAI optimizes ChatGPT for conciseness, action-oriented tasks, and tight JSON schemas. Google, by contrast, builds Gemini from a multimodal origin with an enormous working-memory context, which calls for specific positioning of information.

Those working at a professional level with AI applications or developing software on top of LLM APIs cannot afford to treat all models the same way. Model-specific prompting is the art of leveraging each model's unique strengths while consciously working around its known weaknesses. In this article, we break down how Claude, Gemini, and ChatGPT differ under the hood in how they process instructions, and how you adjust your prompt strategy per provider for the best result.

System cards and alignment: the psychology behind the models

To understand why a model responds in a certain way, you need to look at the "system card" and the alignment philosophy of its maker. Alignment, after all, determines how the model makes choices when instructions are ambiguous or when a conflict arises between different instructions.

For the Claude family, Anthropic applies the principle of Constitutional AI. During training, Claude is judged by another AI model based on an explicit constitution with values such as honesty, harmlessness, and helpfulness. This makes Claude exceptionally good at interpreting nuance and providing transparent justification. However, Claude also tends to be very verbose about what it can and cannot do, and when in doubt, is more likely to give an extensive explanation than a short, definitive answer. In addition, Claude is strongly centered around the idea that the user provides complex context that must be read carefully.

With the GPT series (such as GPT-4o), OpenAI has charted a very pragmatic alignment course. GPT models are trained to respond extremely directly to the most recent instruction in the chat history. They are heavily optimized for task-orientation: if you ask GPT-4o for a list of three points, you'll almost certainly get exactly three points without introductory pleasantries. This "steerability" makes ChatGPT fantastic for structured data processing, but it also makes the model more prone to ignoring background context placed earlier in a long prompt.

Google Gemini was built from the ground up as a native multimodal model capable of processing extremely large context windows. Where Claude and GPT have traditionally been text models to which visual modules were added, Gemini processes text, images, and audio within the same attention layers. Gemini's alignment is strongly geared toward processing massive amounts of information and generating natural, human-like conversations. As a result, however, Gemini can get distracted more quickly by noise in the prompt if the core question is not clearly delineated.

Prompt structure for Anthropic Claude: XML tags and explicit frameworks

Anthropic sets out very explicit guidelines for prompting Claude. The most important tool in your toolkit with Claude is the use of XML tags (such as <context>, <instructions>, and <examples>). Claude's attention layers were specifically trained during the RLHF phase to recognize XML structures as hard boundary markers between different parts of a prompt.

When you give Claude a complex task, using XML tags prevents the model from confusing instructions with the data it needs to process. This is particularly valuable for preventing unwanted influence from the input text. An optimal prompt for Claude is structured roughly as follows:

<system>
Je bent een ervaren senior data-analist gespecialiseerd in de Nederlandse zorgsector.
</system>

<context>
Hieronder staat het jaarverslag van een regionaal ziekenhuis over 2025.
[VOEG HIER DE TEKST IN]
</context>

<instructions>
Analyseer de financiële resultaten in de context.
1. Identificeer de drie grootste kostenposten.
2. Vergelijk de operationele marge met de landelijke norm van 2%.
3. Geef je analyse weer binnen de geopende tags.
</instructions>

<output_format>
Schrijf je antwoord in het volgende XML-formaat:
<analyse>
  <kostenposten></kostenposten>
  <marge_evaluatie></marge_evaluatie>
</analyse>
</output_format>

Claude's weak point with this approach is that the model sometimes carries the XML structure over too literally into its final answer, even when you didn't explicitly ask for that. If you want natural text as the end result, you need to explicitly state: "Do not use XML tags in your final answer to the reader." In addition, Claude tends to formulate a long thinking step first for complex analyses. If you need fast, minimal JSON output, this requires more effort to steer with Claude than with ChatGPT.

Prompt structure for OpenAI ChatGPT: system roles and tight restrictions

OpenAI models respond best to a clear separation between the system role (System Prompt) and the user instruction (User Prompt), combined with clear Markdown headers. Where Claude excels with XML, GPT-4o understands Markdown punctuation such as #, ##, and - down to the last detail.

GPT models have a strong preference for active verbs and explicit restrictions ("Do NOT include..."). When you give GPT-4o a task, an instruction phrased as a negative boundary often works better than with other models. If you want to prevent the model from typing an introductory pleasantry, simply write: "Start directly with the first paragraph. No greeting, no introduction." GPT-4o follows these direct restrictions almost flawlessly.

# ROL
Je bent een professionele copywriter voor Nederlandse B2B SaaS-bedrijven.

# TAAK
Herschrijf de onderstaande productomschrijving voor een landingspagina.

# REGELS
- Maximaal 150 woorden.
- Gebruik de 'Jij'-vorm.
- Banning: gebruik GEEN woorden zoals 'revolutionair', 'naadloos' of 'ontzorgen'.
- Format: Eén H2-kop, gevolgd door twee korte alinea's en een bulletlist van 3 punten.

# INVOER
[TEKST HIER]

Want to know how the latest versions of GPT-4o, Claude, and Gemini perform specifically on Dutch-language writing tasks and style quality? Check out the extensive comparison of GPT-4o, Claude, and Gemini for Dutch content on the hub platform, where the text quality of these three giants is placed side by side.

ChatGPT's weak point is so-called "sycophancy" behavior: the tendency to agree with the user overly quickly. If you formulate an assumption in your prompt ("Why is strategy A better than strategy B?"), GPT-4o will rarely object that strategy B might be superior, unless you explicitly specify in the System Prompt that the model should take on a critical, oppositional role.

Prompt structure for Google Gemini: large context and information placement

Google Gemini stands out for its immense context window, which in many versions ranges from hundreds of thousands to millions of tokens. This fundamentally changes how you structure your prompts. Where with ChatGPT or Claude you often need to summarize or split up information due to context limitations, with Gemini you can send along complete documentation archives, books, or hours of audio transcripts all at once.

This enormous advantage, however, brings with it a specific attention problem known as the "Lost in the Middle" phenomenon. When you provide a gigantic amount of information, the transformer's attention mechanism tends to attach the most value to the beginning and the very end of the prompt. Information hidden deep in the middle of a 500,000-token file can be overlooked if the prompt is structured incorrectly.

The optimal strategy for Gemini is the "Anchored Prompting" method:

1. Start with the main instruction: State the ultimate goal directly in the first paragraph.
2. Provide the bulk context: Place all source documents, data, and background information in the middle block.
3. Repeat the exact question and constraints at the bottom: Close the prompt with the specific question and the desired output format. This anchors the model's attention at the moment text generation begins.

A common weak point of Gemini is that for shorter, everyday prompts it sometimes responds too non-committally or ignores crucial instructions if they're tucked away somewhere in the middle of a chunk of text. By repeating the question at the bottom of the prompt, you force Gemini to filter the entire preceding context based on the most recent instruction.

Reasoning models and Chain-of-Thought per provider

One of the most important breakthroughs in prompting is the use of Chain-of-Thought (CoT), meaning explicitly having intermediate steps written out before the model reaches a final conclusion. However, the way you activate this differs greatly by model and by model generation.

With standard instruction models (such as Claude 3.5 Sonnet, GPT-4o, or Gemini 1.5 Pro), you often need to manually force Chain-of-Thought in your prompt. With Claude, it works excellently to add a specific tag, such as <thinking>, in which the model must first work out its logic before the tag <answer> opens. With GPT-4o, the classic phrase "Think step by step about this problem before you answer" still works wonderfully to reduce logical reasoning errors.

If you want to learn more about the underlying logic of this technique, read the in-depth explanation of having a model reason in steps on the learning platform.

For the newer class of dedicated reasoning models (such as OpenAI's o1 and o3 series), manual Chain-of-Thought in the prompt is actually contraindicated. These models already perform an internal reasoning pass on their own before showing the first token to the user. If you explicitly ask these specific reasoning models to "think step by step" in the prompt, this can disrupt the internal reasoning chain or lead to unnecessary overhead and higher latency. For reasoning models, the rule is: give as bare and clear a problem statement as possible with clear constraints, and let the model organize the thinking space itself.

Tuning sampling parameters and system settings

Prompting doesn't stop at the text of the instruction itself; the parameters with which you call the API or configure the interface are at least as decisive for the end result. The most important settings are Temperature and Top-P.

What many developers overlook is that a setting of Temperature = 0.7 does not produce the same effect on every platform. This is because the behavior of logit probabilities differs by model architecture. An overview of the recommended settings per task and provider:

Want to understand exactly how these settings work mathematically behind the scenes? Check out the practical explanation of temperature, top-p, and other sampling parameters for a deeper dive into the probability distributions of LLM outputs.

Common mistakes in cross-model prompting

In practice, we see many AI users and developers run into the same obstacles when switching between different model providers. An overview of the most critical mistakes and how to fix them:

1. 'Copy-pasting' Claude XML prompts into ChatGPT

When you feed an extensive prompt full of XML tags directly to GPT-4o, the model will understand the tags, but it may either ignore them or randomly carry them over into the output. Rewrite XML tags for GPT-4o into clear Markdown headers (#) and numbered instruction lists.

2. Insufficient instruction repetition in large Gemini prompts

Uploading a 200-page PDF to Gemini with only the prompt "Summarize this" at the top often leads to superficial summaries of just the first chapters. With Gemini, always place the specific summarization instruction and the desired focus points at the very bottom of the text mass.

3. Expecting hallucinations to arise the same way

Each model has different "blind spots." GPT-4o, lacking facts, tends toward convincingly making up plausible details. Claude, when in doubt, tends more toward excessive caution or refusal, while Gemini sometimes incorrectly links facts from different parts of a large source. To understand how to mitigate these risks per model, you can read the extensive analysis of why AI makes things up and how to limit hallucinations.

If you're curious about a broader overview of the pitfalls users encounter in practice, regardless of which model they use, we recommend the article on common prompting mistakes and how to avoid them on the community platform, where developers share real-world experiences.

Summary and comparison matrix

To quickly make the right choices when designing prompts for a specific model, you can use the comparison matrix below as a guide: