How to Adapt One Prompt for ChatGPT, Claude, and Gemini
Keep one model-neutral task specification and adapt context, tools, and output contracts for different AI assistants.
Do not maintain three unrelated prompts
Most business tasks do not need a completely different idea for each model. They need one stable task specification and a thin model-specific layer. The stable layer contains the goal, facts, constraints, and acceptance criteria. The adapter layer covers available tools, context packaging, and output mechanics.
This separation makes prompts easier to test. If the result changes, you can tell whether the task specification changed or the model handled the same specification differently.
Build a model-neutral core
Write the core as if it were a brief handed to a capable contractor. Avoid model brand names and product-specific UI instructions in this part.
- Outcome and target audience.
- Authoritative source material.
- Non-negotiable constraints and forbidden claims.
- Required output structure.
- Acceptance checks and uncertainty behavior.
Compare the three supplied pricing plans for a five-person design agency. Use only the attached plan details. Recommend one plan, explain the two decisive trade-offs, and flag any missing information. Output a comparison table followed by a recommendation under 150 words.
Adapt context and tools
The useful differences are often operational rather than rhetorical. One interface may have web search enabled, another may support a large uploaded document, and another may be embedded in a codebase. Tell the assistant which tools it may use and what counts as an authoritative source.
- If browsing is available, define allowed sources and freshness requirements.
- For long documents, identify the relevant sections and request evidence references.
- For code assistants, point to repository files and require local verification.
- If no external tools are available, prohibit claims that require fresh information.
Use an explicit output contract
Different models may choose different levels of detail. An output contract reduces that variance. Define headings, field names, maximum lengths, table columns, or JSON keys when downstream work depends on a stable shape.
Do not request JSON unless software will consume it. For human work, a compact table and a short decision note are usually easier to review and repair.
Use the files attached in this conversation as the only authority. Cite the filename and section beside every pricing fact. If a requested fact is absent, write “not provided”; do not infer it.
Test with a small evaluation set
A single impressive answer is weak evidence. Keep five to ten representative inputs, including one ambiguous case and one case with missing data. Run the same neutral core through each model adapter and score the output against the same rubric.
Choose a model based on the task and operating cost, not on a universal ranking. The best model for a long policy comparison may not be the best choice for a fast classification step or an in-repository code edit.
- Accuracy against supplied facts.
- Constraint compliance.
- Completeness of the required structure.
- Amount of manual correction required.
- Latency and cost at your expected volume.
Turn the method into a usable prompt
Enter a rough idea and PromptSmith will add structure, constraints, and an output format.
Optimize a prompt free →Apply the method with a ready template
Extract repeated pains, desired outcomes, objections, and exact customer language while separating evidence from interpretation.
Create an evidence-based content brief from search intent, reader tasks, original value, and internal-link opportunities.