Claude and ChatGPT: What They Don’t Tell You About Prompting
Why better AI results depend on context, model choice and judgement more than secret prompt formulas
“Prompting is no longer the art of finding the perfect sentence. It is the discipline of building the right conditions for a useful answer.”
Abstract
Prompting is a workflow, not a single moment. Claude and ChatGPT can both produce exceptional work, yet the same prompt can lead to very different results. This happens because your visible prompt is only one part of a larger system involving the selected model, persistent instructions, conversation history, source material, tools and platform-level settings.
Effective prompt engineering in 2026 therefore involves much more than learning a formula or collecting reusable commands. It requires a clear outcome, relevant context, suitable evidence, defined constraints, examples of quality and a reliable way to evaluate the answer.
For marketers, the competitive advantage increasingly comes from learning how to brief AI, how to recognise weak output and how to develop repeatable human-AI workflows. The real skill is no longer prompt writing alone. It is context engineering, judgement and iterative improvement.
The Prompt Formula Industry Has Oversimplified the Problem
Search for advice on how to prompt Claude or how to prompt ChatGPT and you will quickly find hundreds of frameworks.
Tell the AI to adopt a role. Explain the task. Add context. Specify a format. Request a particular tone.
These techniques remain useful. They are also only the visible part of what is happening.
A prompt that performs brilliantly for one user may deliver an average response for someone else. A carefully developed ChatGPT prompt may feel strangely flat when copied into Claude. A prompt that worked last month may behave differently after a model or platform update.
This can feel inconsistent until we stop treating AI prompting as a single instruction and start viewing it as a complete operating environment.
The most useful prompting lesson is rarely printed on the prompt template:
Your prompt is not the whole prompt.
Your Prompt Sits Inside a Larger Instruction System
When you enter a request into Claude or ChatGPT, the model is already operating within several layers of instructions and context.
These may include:
- platform-level system instructions
- the capabilities and behaviour of the selected model
- your account preferences or custom instructions
- previous messages in the conversation
- files, projects or knowledge sources
- available tools such as web search or data analysis
- your latest visible request
ChatGPT’s Custom Instructions, for example, can apply preferences across conversations. Claude’s consumer interface also uses a system prompt that Anthropic periodically updates, and Anthropic explains that this interface-level system prompt is separate from the behaviour of its API.
This means an identical visible prompt is rarely a perfectly controlled comparison.
One user may have told ChatGPT to use British English, write concise answers and assume an expert audience. Another may have months of project context attached to the conversation. A third may be starting from an entirely blank chat.
The words entered into the message box may match. The complete instruction environment does not.
This is why copying somebody else’s “ultimate ChatGPT prompt” often produces underwhelming results. You have copied the instruction without copying the context that made it successful.
The Model You Choose Is Part of the Prompt
People often ask whether Claude or ChatGPT is better. A more valuable question is:
Which model, operating mode and workflow are most appropriate for this task?
OpenAI distinguishes between reasoning models designed for complex planning and decision-making and faster GPT models designed for well-defined execution. Its own guidance says reasoning models generally respond well to straightforward goals, while execution-focused models benefit from more explicit instructions.
This changes how we should think about prompt writing.
A complex reasoning model can often work from a high-level brief:
Analyse these three potential market-entry strategies and recommend the strongest option based on commercial viability, implementation risk and likely customer adoption.
A more execution-focused model may perform better when given the precise steps, format and rules it should follow:
Compare the three strategies using a table with the columns Market Potential, Cost, Time to Implement, Operational Risk and Recommendation. Score each category from one to five and provide a 150-word explanation beneath the table.
Neither approach is inherently superior. Each matches a different kind of model behaviour and a different stage of the work.
The model is not simply the place where the prompt is submitted. Model selection forms part of the prompt architecture itself.
Longer Prompts Do Not Automatically Produce Better Answers
The growth of prompt engineering has created a belief that more instructions create more intelligence. They often create more opportunities for conflict.
Long prompts can be extremely valuable when the task genuinely requires detailed context, multiple constraints, source material and a defined output structure. Length becomes less useful when the prompt repeats itself, contains vague adjectives or combines several competing objectives.
Consider this instruction:
Write me an amazing, engaging, authoritative, highly compelling and completely original LinkedIn post that will go viral and establish me as a leading expert.
It contains plenty of ambition and very little usable direction.
What does “engaging” mean for this audience? What evidence establishes authority? Is the objective reach, conversation, leads or profile positioning? What does the audience already understand? Which opinions should the post challenge or reinforce?
A stronger prompt provides information that influences the answer:
Write a 700-word LinkedIn article for UK marketing managers who already use generative AI for content production but have limited experience of agentic workflows. Explain why AI agents require process design rather than additional prompting. Use a commercially grounded, educational tone, include one practical marketing example and end with a question that encourages senior marketers to share their experience.
The second prompt is longer because it contains decision-making context. Every part has a job.
“The best prompt is not the longest. It is the one that removes the most expensive ambiguity.”
Context Is More Valuable Than Decorative Language
Generative AI can write polished sentences with very little help. The greater challenge is making those sentences relevant, accurate and commercially useful. Context supplies the difference.
For marketing tasks, useful context may include:
- the organisation’s commercial objective
- the target audience and its level of knowledge
- the product’s genuine points of difference
- previous campaign performance
- customer language and objections
- competitor positioning
- brand voice examples
- legal or regulatory constraints
- the action the content should encourage
This explains why prompting improvements often plateau. The user continues editing the instruction while the model still lacks the evidence needed to do better work.
Changing “write an authoritative article” to “write a deeply authoritative article” adds emphasis. Supplying original research, customer questions, market data and examples of previous high-performing content adds intelligence.
The quality of an AI response is frequently limited by the quality of the environment we create around the task.
Examples Can Communicate Quality Better Than Adjectives
One of the most effective ways to improve Claude or ChatGPT output is to provide an example of the result you consider good.
A phrase such as “write in a premium editorial style” remains open to interpretation. A relevant example shows sentence length, vocabulary, structure, pacing and depth.
Anthropic describes examples as one of the most reliable ways to steer Claude’s format, tone and structure. Its current guidance recommends using relevant and varied examples and separating them clearly from the rest of the prompt.
OpenAI similarly recommends using examples when more complex output requirements cannot be achieved reliably through a direct instruction, although its reasoning-model guidance suggests trying a clear zero-shot prompt first.
This creates a sensible sequence:
Start with a clear request.
Review the result.
Add an example when the model continues to misinterpret the required tone, structure or level of detail.
Examples work because they convert an abstract preference into observable evidence. They show the standard rather than merely describing it.
Claude and ChatGPT Do Not Always Want the Same Prompt
Both systems respond well to clear goals, context and output requirements. Their official guidance also reveals several practical differences.
How to Prompt ChatGPT More Effectively
For a reasoning model within ChatGPT or the OpenAI ecosystem, begin with a direct description of the outcome.
State the problem, the relevant constraints and the definition of success. Allow the model to plan the route.
OpenAI specifically advises users of reasoning models to keep prompts straightforward, avoid unnecessary requests to “think step by step”, use clear delimiters and try a zero-shot instruction before adding examples.
A good reasoning prompt might therefore say:
Assess the proposed marketing strategy using the attached customer research and budget. Identify the three assumptions most likely to undermine performance. Recommend practical changes that keep total expenditure below £50,000. Distinguish evidence from inference and flag any information that requires verification.
For a tightly defined production task, greater specificity becomes useful:
Turn the approved strategy into a 12-week content plan. Include one long-form article and three supporting social posts per week. Present the plan as a table containing week, theme, audience problem, article title, social angles and primary call to action.
Persistent preferences such as language, house style and formatting can sit within ChatGPT’s Custom Instructions or an appropriate project environment. The individual prompt can then focus on the current task rather than restating every personal preference.
How to Prompt Claude More Effectively
Anthropic advises users to be clear and direct, explain relevant context and specify the required output and constraints. It also recommends XML tags for complex prompts containing several kinds of information.
A structured Claude prompt might look like this:
<context>
We are developing a campaign for UK marketing managers who understand
generative AI but have limited experience of AI agents.
</context>
<objective>
Generate a commercially credible webinar proposition that can attract
both strategic marketing managers and tactical marketing executives.
</objective>
<source_material>
Insert research, audience feedback and previous webinar data here.
</source_material>
<instructions>
Develop the webinar title, proposition, learning outcomes, agenda and
three promotional messages.
</instructions>
<constraints>
Use British English. Avoid exaggerated claims. Keep the recommendations
practical for organisations with limited AI maturity.
</constraints>
<quality_check>
Confirm that every agenda section offers value to both strategic and
tactical marketers.
</quality_check>
XML tags are not magic words. They simply make the boundaries between context, evidence, instructions and output requirements easier for Claude to interpret.
Anthropic also gives specific guidance for very large documents. It recommends placing long source material towards the beginning of the prompt and positioning the query later, particularly when several documents are involved.
That detail can make a meaningful difference when asking Claude to analyse reports, transcripts, research documents or lengthy strategy files.
“Think Step by Step” Has Stopped Being Universal Advice
For several years, “think step by step” was one of the most frequently shared prompt engineering tips. Its value now depends on the model. OpenAI says its reasoning models already carry out internal reasoning and that asking them to expose or manufacture a step-by-step thought process may add little value and can sometimes reduce performance. The preferred approach is to specify the goal, constraints and success criteria clearly.
Claude’s guidance is more conditional. Anthropic describes manual step-by-step prompting as a possible fallback when extended thinking is unavailable, while its more advanced models can use adaptive thinking according to task complexity.
A more robust instruction for either platform is:
Before finalising the answer, check it against the stated criteria and correct any important omissions or inconsistencies.
This asks for quality control without attempting to dictate the model’s private reasoning process.
For high-stakes work, ask for an auditable output:
- identify the evidence used
- show calculations
- distinguish fact from inference
- state assumptions
- flag uncertainty
- cite verifiable sources
- explain the basis of the final recommendation
You need to see enough to evaluate the work. You do not need a theatrical transcript of every internal thought.
Prompting Is a Workflow, Not a Single Moment
A strong first prompt helps. Professional-quality output usually develops through a sequence. The initial response gives you something concrete to assess. You can then ask the model to identify weaknesses, compare the answer with the original brief, strengthen weak sections and test its assumptions.
A useful workflow may involve:
- defining the problem and success criteria
- supplying context and evidence
- generating an initial response
- evaluating it against the criteria
- correcting factual or strategic weaknesses
- adapting the final output for its intended channel
This feels less glamorous than discovering a secret one-line command. It is far more dependable.
Both Anthropic and OpenAI place evaluation near the centre of effective prompt development. Anthropic recommends defining success criteria and having a way to test them before refining a prompt. OpenAI’s evaluation guidance similarly follows a cycle of defining the task, testing it with representative inputs, analysing results and improving the prompt.
Prompting without evaluation produces content. Prompting with evaluation produces a working capability.
The Six-Layer Prompting Stack
A practical way to improve Claude and ChatGPT prompting is to think in six layers.
1. Platform and Model
Choose the system that suits the task.
Are you asking for fast execution, deep analysis, document synthesis, creative development, research or a multi-stage agentic action?
A poor model-task match cannot always be rescued with better wording.
2. Persistent Instructions
Place stable preferences in the appropriate long-term environment.
These might include your brand voice, preferred language, audience assumptions, formatting rules and recurring quality standards.
This keeps individual prompts focused and reduces unnecessary repetition.
3. Intended Outcome
State what the work must achieve.
“Write a blog post” describes an activity.
“Create a search-optimised article that helps marketing managers compare Claude and ChatGPT prompting methods and positions the author as a credible AI marketing educator” defines an outcome.
4. Context and Evidence
Supply the information the model needs to make good decisions.
Include source material, audience insight, performance data, product information, constraints and relevant examples.
5. Output Contract
Define what the finished response should contain.
Specify length, structure, level of detail, format, audience, tone and required components.
The word “contract” is useful because it encourages precision. You should be able to inspect the final answer and decide whether the requested elements are present.
6. Evaluation
Explain how quality will be judged.
Ask the model to verify claims, test recommendations against constraints, identify unsupported assumptions and check that every requested component has been completed.
This final layer is frequently missing. It is also where much of the improvement happens.
A Portable Prompt Framework for Marketers
The following structure works as a practical starting point for both Claude and ChatGPT:
GOAL
Describe the business or communication outcome.
AUDIENCE
Explain who the work is for, what they understand and what they need.
CONTEXT
Provide the background information that influences the task.
SOURCE MATERIAL
Include the evidence, research, examples or documents the model should use.
TASK
State exactly what the model should produce or decide.
CONSTRAINTS
Set boundaries relating to budget, claims, language, tone, length or compliance.
OUTPUT
Define the required format and components.
QUALITY CHECK
Explain how the answer should be tested before it is finalised.
For Claude, you may choose to wrap these sections in descriptive XML tags.
For a ChatGPT reasoning model, you may be able to shorten the instructions once the goal, evidence and success criteria are clear.
The framework provides a starting architecture. It should be adapted to the task rather than followed as a ceremony.
Can Claude or ChatGPT Improve Your Prompt for You?
Yes, within limits. Both systems can review a prompt, identify ambiguity and propose a clearer structure. Anthropic even provides prompt generation and prompt improvement tools within its developer environment. OpenAI recommends treating production prompts like managed, testable application components rather than casual fragments of text.
AI can identify that your audience is missing, your output format is vague or your constraints conflict.
It cannot automatically know the commercial information you have failed to provide.
Ask the model to improve a weak prompt and it may produce a beautifully organised version of the same incomplete brief. The wording improves while the knowledge gap remains. The human contribution is deciding what the model needs to know.
Five Prompting Mistakes That Reduce Marketing Quality
Treating a Role as a Substitute for Expertise
“Act as a world-class marketing strategist” may influence vocabulary and tone. It does not provide customer research, market evidence or commercial understanding.
Asking for Too Much in One Response
Research, strategy, campaign planning, copywriting and quality assurance often benefit from separate stages. Dividing the work makes errors easier to identify.
Failing to Set Source Boundaries
Tell the model whether it should rely solely on supplied information, use general knowledge or conduct current research. Otherwise, plausible invention can slip into an apparently authoritative answer.
Judging Fluency as Accuracy
Claude and ChatGPT can both write confident, polished prose. Smooth language remains a presentation quality rather than proof that the underlying reasoning is correct.
Saving Prompts Without Saving Their Context
A reusable prompt should explain its required inputs, intended model, success criteria and examples. Saving only the command removes much of the system that made it work.
Prompt Engineering Is Becoming Context Engineering
The phrase “prompt engineering” suggests that success comes from arranging words in a clever sequence. Modern AI use is moving towards a broader discipline.
Context engineering involves deciding:
- which model should handle the task
- what information it should receive
- which instructions should remain persistent
- what tools it can access
- how the task should be divided
- how the output will be evaluated
- where human judgement must remain
This is particularly important for marketers. AI can accelerate research, segmentation, content production, campaign analysis and customer communication. Sustainable value emerges when those capabilities are connected to a sound marketing process.
A weak process performed faster remains a weak process. A well-designed process supported by relevant data, clear decision rules and intelligent human oversight can become a genuine competitive advantage.
Claude vs ChatGPT Prompting: Which Is Better?
Neither platform has a universally superior prompting method. Claude often responds particularly well to carefully organised context, examples and clearly separated document sections. ChatGPT’s ideal prompting style varies according to whether the selected model is performing complex reasoning or executing a well-defined production task.
The strongest results come from understanding the behaviour of the model in front of you and adapting the brief accordingly. This also means organisations should be cautious about creating one enormous universal prompt and deploying it everywhere.
A useful prompt library records:
- the task it supports
- the platform and model used
- required source inputs
- example outputs
- known failure patterns
- evaluation criteria
- version history
That is less like collecting clever prompts and more like developing organisational intellectual property. Which is precisely what it should become.
The Real Human Skill Behind Better AI Output
Prompting is regularly presented as a technical skill. Its foundations are deeply human. You need to:
- Define a problem clearly.
- Understand the audience.
- Select relevant evidence.
- Recognise when an answer is superficial.
- Challenge assumptions and make commercial judgements.
- Know what good looks like.
Claude and ChatGPT can help structure a brief. They can suggest missing questions. They can evaluate an answer against stated criteria. They cannot reliably supply the judgement you have never defined.
The future advantage will not belong to the person with the largest folder of prompts. It will belong to the person who can create a clear brief, supply meaningful context, select the right model and evaluate the result with confidence. That is what they rarely tell you about prompting. The words matter, but the thinking around them matters considerably more.
Frequently Asked Questions
Is Claude better than ChatGPT for prompting?
Claude and ChatGPT have different strengths and model behaviours. Claude often benefits from structured context, examples and XML tags for complex prompts. ChatGPT prompting depends partly on whether you are using a reasoning model for complex analysis or a faster model for defined execution. The better option depends on the task, evidence, tools and required output.
Do Claude and ChatGPT need different prompts?
The same basic principles apply to both: define the goal, provide context, set constraints and specify the output. The final structure may differ. Claude’s official guidance places particular emphasis on XML tags, examples and the positioning of long documents. OpenAI recommends simple, direct prompts for reasoning models and more precise instructions for execution-focused GPT models.
Should I tell ChatGPT to think step by step?
OpenAI advises that this is generally unnecessary for its reasoning models because they already perform internal reasoning. A more useful instruction is to define the end goal and ask the model to verify the final answer against clear success criteria.
How long should an AI prompt be?
A prompt should be long enough to include the information that materially affects the result. Short prompts work well for clear, simple tasks. Complex assignments may require detailed context, evidence, constraints and examples. Repetition and decorative adjectives add length without necessarily improving quality.
What is the best prompt framework for marketers?
A practical marketing prompt should cover the goal, audience, context, source material, task, constraints, output format and quality check. These components help the model connect its response to a genuine marketing objective.
Why does the same prompt produce different results?
Results can vary because of model choice, system instructions, account settings, conversation history, attached files, tool availability and the probabilistic nature of generative AI. The visible prompt is only one part of the complete instruction environment.
What is the difference between prompt engineering and context engineering?
Prompt engineering focuses primarily on the wording and structure of an instruction. Context engineering covers the wider system, including model selection, source information, persistent instructions, tools, workflow design and evaluation. Context engineering offers a more complete description of professional AI use in 2026.
