AI-Assisted Adobe Solution Design
[OVERVIEW]
I built this prompt to help turn a complex business or technical requirement into a clear Adobe solution design.
The idea is to avoid jumping straight into a product feature or implementation. The prompt first breaks down what the business is actually trying to achieve, then looks at the data, platform responsibilities, constraints, and the best way to structure the solution across the relevant Adobe capabilities.
It also asks the AI to explain why the most obvious approach may not work, use official Adobe documentation to support the analysis, and challenge its own recommendation before producing the final design.
The output can then be used as the basis for a technical discussion or turned into a concise, slide-ready solution.
[IMPACT]
The main goal was to make solution design more structured, especially when a requirement involves several Adobe products or capabilities and it is not immediately clear where each responsibility should lie in it.
I wanted the prompt to help move from a broad business requirement to a solution that explains the logic behind the architecture, rather than simply listing product features.
The result was a repeatable way to:
- break down the requirement before designing the solution.
- identify why a direct or initial approach may not be suitable.
- separate responsibilities such as data processing, audience creation, activation and measurement.
- identify dependencies, limitations and points that still need validation.
- keep the final solution concise enough to communicate to both technical and non-technical stakeholders.
It also helped make the reasoning behind the recommendation.
[LESSONS]
One of the main things I learned is that the quality of the solution improves when the prompt starts with the business problem rather than the product.
If the prompt starts by assuming which Adobe capability should be used, the answer can become too focused on making that product fit the requirement. Asking first what needs to happen, where the data lives, and which system should own each responsibility leads to a more balanced design.
It was also useful to explicitly ask why the obvious approach might fail. That often surfaces product limitations, data constraints or operational issues that are easy to miss at first.
Another improvement was asking the AI to challenge its own recommendation before finalising the answer. This helps identify assumptions, unnecessary complexity, or alternative approaches that may have been overlooked.
Using official Adobe documentation as the main source also makes it easier to separate confirmed product behaviour from solution design judgement.
I am still refining the prompt as I use it with different scenarios, especially around how it handles missing information, product dependencies and the balance between technical detail and a solution that is easy to present.
[TRYABLE]
1. Start with a real business or technical challenge that needs to be translated into an Adobe solution design.
2. Remove any sensitive or proprietary information before using the prompt. Do not include customer names, credentials, internal URLs, tenant IDs, production configuration, project identifiers, or confidential architecture details.
Replace the placeholders in the prompt with:
- the business requirement or use case
- the Adobe products already in scope, if known
- the maximum number of slides or output length required
3. Run the prompt in an AI tool that can access current Adobe documentation.
4. Review the first part of the answer before looking at the final recommendation. Check that the AI has correctly understood the business objective, data requirements, and platform constraints.
5. Pay particular attention to the section explaining why the obvious or current approach may not work. This is often where important product, data, or operational limitations are identified.
6. Review how responsibilities are split across the Adobe capabilities involved. Check that one product is not being forced to handle processing, activation, analysis, and measurement simply because it is already part of the solution.
7. Review the Challenge the AI section and check whether the recommendation changes after the model questions its own assumptions.
8. Validate the official Adobe documentation referenced in the response, especially for product capabilities, limitations, licensing, entitlements, and data retention.
9. Use the output as the basis for a technical review, workshop, or presentation. Refine the prompt and rerun it if important context is missing or if the proposed solution still contains assumptions that need validation.
10. If a presentation is required, use the slide-ready output as a starting point and adapt the level of detail to the audience rather than copying the response directly into slides.
[SETUP]
Chatgpt/Claude/Gemini
[SAMPLE_OUTPUT]
Act as a Senior Adobe Experience Cloud Solution Architect with strong functional and technical knowledge of Adobe Customer Journey Analytics, Adobe Experience Platform, Real-Time CDP, Data Distiller, and related Adobe capabilities.
BUSINESS CHALLENGE
[Describe the business or technical use case]
ADOBE PRODUCTS IN SCOPE
[List the Adobe products already involved, if known]
OUTPUT CONSTRAINT
Create a solution design that can be presented in no more than [NUMBER] slides.
OBJECTIVE
Turn the business requirement into a technically sound and concise Adobe solution design.
Do not jump directly to a product feature or implementation.
First determine:
- What the business is actually trying to achieve
- What data is required and where it lives
- Why the obvious or current approach may not work
- Which Adobe capability should own each responsibility
- How the solution can operate reliably in production
SOURCE REQUIREMENTS
Base the analysis primarily on current official Adobe documentation, especially Adobe Experience League.
For important technical conclusions:
- Reference the official Adobe documentation when possible
- Provide the documentation URL
- Distinguish between:
1. Documented Adobe capabilities
2. Solution architecture recommendations
3. Assumptions requiring validation
If something cannot be confirmed through official documentation, state this clearly.
ANALYSIS
1. Understand the requirement
Identify:
- Business objective
- Required customer or user outcome
- Data required
- Historical depth
- Refresh frequency
- Activation needs
- Measurement needs
2. Explain why the direct approach may fail
Check for:
- Data availability
- Retention limitations
- Segmentation limitations
- Profile vs analytical data
- Computation complexity
- Audience persistence
- Refresh cadence
- Activation constraints
3. Design the target methodology
Determine which Adobe capability should manage:
- Data storage
- Historical analysis
- Computation
- Profile enrichment
- Audience creation
- Activation
- Measurement
Do not force one Adobe product to perform every responsibility.
4. Production readiness
Assess:
- Scheduling
- Refresh cadence
- Data freshness
- Scalability
- Governance
- Operational ownership
- Product dependencies
- Licensing or entitlement requirements
5. Architecture
Provide a simple end-to-end flow such as:
Data → Processing → Persisted Result → Audience → Activation → Measurement
Explain the responsibility of each component.
6. Challenge the initial solution
Ask:
- Are we solving the business problem or just reproducing the current technical approach?
- Is each Adobe capability being used for its intended purpose?
- Are we introducing unnecessary complexity?
- Could computation and activation be separated?
- Is there a simpler or more maintainable solution?
7. CHALLENGE THE AI
Before finalising the design, critically review your own recommendation.
Ask:
- What is the strongest argument against my solution?
- Which assumption could materially change the design?
- Have I chosen a product simply because it is available?
- Am I confusing analytical capability with activation capability?
- Does the recommendation depend on an entitlement that may not be available?
- Is any part technically possible but operationally unsuitable?
- Could the architecture be simplified?
Revise the recommendation if this review reveals a better approach.
OUTPUT
Provide:
1. Executive summary
2. Business requirement interpretation
3. Why the direct approach may fail
4. Recommended methodology
5. Architecture
6. Production considerations
7. Challenge of the initial solution
8. Challenge of the AI recommendation
9. Open questions / validations
10. Official Adobe sources consulted
Make the final output slide-ready and suitable for both technical and non-technical stakeholders.
Prioritise technical accuracy, simplicity, maintainability, and clear business reasoning over trying to use as many Adobe products as possible.