A prompt is not a universal command. It is an instruction interpreted by a particular model, product interface, system message, tool configuration, and set of defaults. Claude and ChatGPT therefore can produce different wording, depth, formatting, refusals, or actions from the same text. The reliable solution is not to search for one magic prompt, but to keep a clear task specification and adapt the parts that depend on the exact model and interface.
Start with the same task, context, constraints, and output contract for both products. Then test that baseline on named model versions, score the results against identical criteria, and make small, targeted changes. Anthropic and OpenAI both recommend direct instructions, relevant examples, and well-structured context, but their documentation is model-specific rather than proof that one product family is universally better.
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Why the same prompt diverges
Claude and ChatGPT are product families, not single unchanging models. Each family contains multiple model generations, and the behavior can also change with the web interface, an API request, enabled tools, conversation history, system or developer instructions, temperature-like settings, and safety policies. A prompt copied from one environment may therefore be interpreted under a different instruction hierarchy or with different assumptions in another.
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Output variation also comes from generation itself. A model predicts a sequence of tokens, and small changes in context or sampling can alter later choices. In a long conversation, earlier turns and hidden application instructions can matter as much as the prompt you can see. For that reason, compare exact model and request configurations rather than treating Claude or ChatGPT as a single stable personality.
Official guidance supports adapting prompts to the selected model. Anthropic describes its page as guidance for current Claude models and warns that techniques named for a particular model should be checked with your own evaluations. OpenAI likewise distinguishes model families and explains that reasoning models behave differently from chat models. Neither source establishes a universal Claude-versus-ChatGPT performance ranking.
What Anthropic recommends for Claude
Use direct, explicit instructions
Anthropic’s Prompting best practices says, “Claude responds well to clear, explicit instructions.” State the job, the audience, the required decisions, the boundaries, and the form of the answer. Replace indirect requests such as “Could you take a look at this?” with an action and a success condition: “Extract the three risks, quote the supporting sentence for each, and return a table with columns for risk, evidence, and mitigation.”
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Show the desired pattern with examples
Anthropic recommends relevant, diverse, well-structured examples. Its guide suggests trying three to five examples as a practical starting point; that is the vendor’s guidance, not a universal optimum. Examples should cover the kinds of inputs that are easy to mis-handle, including edge cases, rather than repeating the same ordinary case.
Separate each demonstration from the instructions so Claude cannot mistake an example for a new command. Include both the input and the ideal output, and make the formatting exact. If the answer must be valid JSON, show valid JSON in every example and say that no commentary should appear outside it.
Use XML tags for complex prompts
For long or mixed prompts, Anthropic recommends descriptive XML tags. Tags make the boundaries between instructions, context, examples, and the current input visible:
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<rules>Choose exactly one category. Do not invent account details.</rules>
<examples>...</examples>
<message>...</message>
<output_format>Return JSON with category and evidence.</output_format>Use names that describe the content, such as <source_document>, <instructions>, and <user_input>. XML is not a guarantee of better answers; it is a way to reduce ambiguity when a prompt contains several kinds of material.
Ground long-document answers in the supplied text
For document analysis, label the document and ask for evidence from it. Request short supporting quotations or section names when factual traceability is important. Anthropic’s guide reports an internal test in which placing the query at the end of a long, complex, multi-document prompt improved response quality by up to 30 percent; the claim is from Anthropic’s current guide, the year is not stated on the page, and it applies to that described setup rather than to a Claude-versus-ChatGPT comparison. Treat it as a technique to test, not a guaranteed gain.
What OpenAI recommends for ChatGPT and the API
Separate high-level instructions from the input
OpenAI’s Prompt engineering guide demonstrates a developer message divided into identity, instructions, and examples. Its Text generation guide explains that the API’s instructions parameter carries high-level behavioral guidance such as goals, tone, and examples, with priority over the input parameter.
This distinction is directly applicable to API calls. In the ChatGPT interface, you may not have a field literally named instructions; use the available system, developer, or custom-instruction controls when provided, and put the task-specific material in the user message. Do not assume that pasting an API parameter name into a normal chat creates the same instruction hierarchy.
Give the model the logic and data it needs
OpenAI’s current-model guidance says precise instructions should include the logic and data needed for the task. State how to resolve ties, what counts as sufficient evidence, how to handle missing fields, and which sources are allowed. If the answer must use information outside the model’s training data, supply that context or retrieve it with an appropriate application workflow; a request to “know the latest” is not a substitute for providing current data.
Use few-shot examples strategically
OpenAI describes few-shot prompting as a way to steer behavior with input/output examples and recommends showing a diverse range of possible inputs. Include examples when a rule is difficult to express, when a format is unusual, or when borderline cases matter. Keep examples consistent with the written rules. A contradictory example can override the clarity you thought you added.
Account for reasoning and chat model differences
OpenAI states that reasoning models behave differently from chat models and that both model and API choice matter. A reasoning-oriented model may need a clear objective, relevant data, and success criteria rather than a long script telling it how to perform every hidden step. Ask for the result and any concise justification you need; do not require private chain-of-thought. If you need an auditable answer, request a short rationale, cited evidence, intermediate values, or a validation checklist instead.
Documented emphasis at a glance
| Prompt element | Anthropic’s documented emphasis | OpenAI’s documented emphasis |
|---|---|---|
| Core instruction | Clear, explicit directions and a precise description of the desired output | Identity, instructions, examples, and the logic and data required for the task |
| Examples | Relevant, diverse examples; the guide suggests three to five as a starting point | Few-shot examples showing a diverse range of possible inputs |
| Structure | Descriptive XML tags for complex prompts and long documents | Distinct instruction and example sections, commonly demonstrated with Markdown and XML |
| Model selection | Techniques tied to a named Claude model should be checked on the model you use | Reasoning and chat models can need different prompting approaches; model and API choice matter |
| Instruction priority | Follow the selected Claude interface’s documented message structure | The API’s instructions parameter has higher priority than input; a normal ChatGPT chat does not automatically expose that exact parameter |
These are documented recommendations, not a controlled comparison. They explain why a prompt may need different packaging without proving that one family wins on a general benchmark.
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Build a portable baseline prompt
Before adding product-specific syntax, write one version that makes the task independently understandable. Use four labeled parts:
- Task: the action and the outcome, expressed as a verb and a deliverable.
- Context: the audience, definitions, source material, date, and other facts the model may use.
- Constraints: limits on evidence, length, tone, safety, uncertainty, and prohibited assumptions.
- Output format: the exact headings, fields, ordering, units, and serialization rules.
Add examples only when a rule or format would otherwise be ambiguous. A portable baseline can look like this:
Task: Extract the customer’s three requested actions from the message.
Context:
- Audience: an internal support agent
- Use only the message below; do not infer account facts
Constraints:
- Preserve the customer’s wording where possible
- If fewer than three actions appear, return only those present
- Mark uncertainty as “unclear”
Output format:
- Return a JSON array of objects with “action” and “evidence” fields
- Return JSON only, with no surrounding explanation
Message:
{{message}}The double-curly variable is a template placeholder, not text to send unchanged unless your application replaces it. Keep the baseline stable while you test model-specific adaptations; otherwise you will not know which change caused an output difference.
Adapt that baseline for Claude
- Wrap mixed material in descriptive tags. Put the task, rules, examples, source, and current input in separate XML elements. Keep the tag names meaningful and consistently ordered.
- Move the requested query after long source material. For a large document, place the final question or extraction instruction after the relevant text, then ask for evidence from that text.
- Make the output contract explicit. Say whether commentary is allowed, how many items to return, what to do with missing data, and how to represent uncertainty.
- Add three to five varied examples when needed. Include at least one boundary case and ensure every example obeys the same rules.
- Check model-specific advice. If Anthropic’s guide names a technique for a particular Claude model, test it on the exact model and interface in your application rather than assuming it transfers to every Claude release.
A Claude-oriented version of the baseline might be:
<task>Extract the customer’s requested actions.</task>
<context>You are assisting an internal support agent. Use only the supplied message.</context>
<constraints>
<rule>Return no more than three actions.</rule>
<rule>Do not infer account facts.</rule>
<rule>Use “unclear” when the wording does not establish an action.</rule>
</constraints>
<output_format>Return JSON only: an array of objects with action and evidence fields.</output_format>
<message>{{message}}</message>
<request>Now produce the JSON array.</request>Adapt that baseline for ChatGPT
- Put stable behavior in the highest available instruction layer. In an API request, place role, goals, constraints, and format rules in
instructionsor the appropriate higher-authority message, then send the changing document as input. In the ChatGPT interface, use its available system or developer controls and keep the user message focused on the current task. - State the decision logic. Tell the model how to count actions, resolve overlapping requests, treat quoted text, and handle an empty result. Precise logic is more reliable than a general request to “be accurate.”
- Supply relevant context. Include the data needed for the answer or connect the application to a selected resource. Do not rely on an unsupported assumption that the model has current or private information.
- Use diverse few-shot examples. Demonstrate normal, ambiguous, and empty cases. Keep the examples in the same format as the required output.
- Choose the model family deliberately. If you switch between a chat model and a reasoning model, retest the prompt. A prompt that micromanages a conversational style may be less useful than one that states the objective, evidence, and acceptance tests for a reasoning model.
An API-style arrangement can be represented as:
instructions: You extract requested actions for an internal support agent. Use only the supplied message. Return JSON only with action and evidence fields. Return no more than three actions, do not infer account facts, and use “unclear” when needed. Follow the examples.
input: Example 1 input and output; Example 2 input and output; then the current message and the request to produce the JSON array.The exact field names and message options depend on the API version you use. Verify them in the current OpenAI documentation rather than assuming that a ChatGPT web conversation and an API call have identical controls.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A repeatable cross-model testing workflow
Prompt adaptation is an evaluation loop, not a one-time rewrite. Use the same task set and scoring rules every time.
- Freeze the baseline. Save the exact prompt, source inputs, examples, and expected output contract. Do not edit it while collecting the first comparison.
- Record the environment. Write down the exact model name or version, date, web interface or API, message roles, relevant settings, enabled tools, and conversation state. A result without this context is difficult to reproduce.
- Use a representative task set. Include ordinary cases, long inputs, missing information, ambiguous wording, and format-sensitive cases. One successful example cannot establish portability.
- Run each task under matched conditions. Keep the input and evaluation criteria identical. Start a fresh conversation when history could influence the result, or deliberately test the same history on both sides and record that choice.
- Score observable outcomes. Separate factual correctness from style preference. Check whether the answer completed the task, followed constraints, used the requested format, handled uncertainty, and took the requested tool action when applicable.
- Make one targeted edit. If Claude confuses source text with instructions, add clearer XML boundaries. If an API call ignores a format rule, move that rule to the higher instruction layer or add a format example. Avoid changing five variables at once.
- Re-run the full set. Keep the edit only if it improves the failed criterion without causing unacceptable regressions elsewhere. Repeat until the remaining differences are acceptable for the use case.
| Evaluation axis | What to inspect |
|---|---|
| Task success and factual correctness | Did the response answer the requested question using the allowed evidence? |
| Instruction and constraint following | Did it obey limits, exclusions, uncertainty rules, and decision logic? |
| Format and length | Is the output valid JSON, a complete table, the required headings, or the specified range? |
| Tone and explanation | Is the level of detail appropriate for the audience, without treating personal preference as factual superiority? |
| Tool or action behavior | When tools are available, did the model select and use the requested action correctly? |
| Consistency | Do repeated runs stay within an acceptable range for the task? |
Troubleshoot by symptom
| Symptom | Likely cause | Targeted adjustment |
|---|---|---|
| Correct facts, wrong format | The format is described as a preference or examples conflict with the rule | State the schema, ordering, cardinality, and whether commentary is forbidden; add a valid example and validate the result in code. |
| Instructions are ignored after a long document | Boundaries between source and command are unclear, or the request is buried | Use descriptive XML or clearly labeled sections and place the final query after the source; ask for evidence from the supplied text. |
| Claude treats an example as a live command | The demonstration is not delimited | Wrap examples in an explicit examples block and label input and output separately. |
| ChatGPT gives a plausible but unsupported answer | Required data or an evidence rule was not supplied | Provide the relevant context, identify allowed sources, and require an evidence field or an “unknown” result. |
| A reasoning model produces a long process instead of the deliverable | The prompt requests hidden step-by-step reasoning or over-specifies internal steps | Ask for the final result plus concise checks, cited evidence, or a short rationale. |
| One interface follows the rule and another does not | Different message roles, system text, tools, or settings | Compare the full request envelope, not just the visible user prompt, and retest with the exact production configuration. |
| Results vary between runs | Sampling, context, tool results, or ambiguous acceptance criteria | Define measurable criteria, reduce unnecessary degrees of freedom, record settings, and evaluate several runs rather than one output. |
Do prompts need to change for reasoning models?
Yes, sometimes, but the change should follow the task rather than a stereotype about a brand. OpenAI’s documentation explicitly says reasoning models and chat models behave differently. For a reasoning model, provide the objective, definitions, data, constraints, and a way to verify success. A long instruction sequence that dictates every internal step can distract from the actual acceptance criteria. Request only the explanation that a reader needs, such as a short justification or a list of checks.
When moving a prompt from a reasoning model to a conversational model, you may need to make the desired structure and tone more explicit. When moving in the other direction, remove decorative instructions that do not affect the result and preserve the data and tests that do. In both cases, evaluate the output rather than assuming that a particular prompting style is inherently superior.
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Should you use XML tags in every prompt?
No. XML is most useful when a prompt mixes several content types or contains long documents, multiple examples, and strict boundaries. A short request with one task and one input may be clearer as ordinary prose or a small Markdown list. Tags add structure, but they do not repair an incomplete task definition, missing data, contradictory rules, or an impossible output requirement.
Use the simplest structure that makes the instruction hierarchy unambiguous. If you later add examples, source documents, or multiple inputs, introduce labeled sections or XML without rewriting the underlying task.
How to interpret differences fairly
A difference in style is not automatically a difference in capability. One answer may use headings while another uses paragraphs; one may be more cautious because its configuration asks it to be; one may ask a clarifying question while another makes a stated assumption. Decide in advance which properties matter for your application, then score those properties consistently.
Keep vendor guidance in its proper scope. The Anthropic and OpenAI pages explain techniques for their own models and interfaces. They do not provide an independent, controlled statistic showing that Claude or ChatGPT is better overall, and no broad success rate or ranking should be inferred from a single prompt comparison. Product defaults and documentation can change, so preserve the model, interface, date, and settings with your evaluation record.
Quick Recap
Portable-prompt checklist
- Have you named the exact task and the outcome that counts as success?
- Did you provide definitions, dates, source material, and the data the model is allowed to use?
- Are constraints measurable, including length, fields, ordering, uncertainty, and prohibited assumptions?
- Is the output format shown in an unambiguous schema or example?
- Are examples relevant, diverse, and clearly separated from live instructions?
- For long Claude prompts, are instructions, context, examples, and inputs separated with descriptive tags?
- For ChatGPT/API use, are stable rules in the appropriate higher-authority instruction layer?
- Have you identified whether the selected model is a chat model or a reasoning model?
- Did you test the same tasks on exact model versions and record interface and settings?
- Are you judging factual correctness and constraint following separately from personal style preference?
- When a failure appears, will you change one targeted prompt element and rerun the full task set?
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