One thing I've noticed while experimenting with AI-generated marketing content: adding more adjectives rarely fixes a bad prompt.
The biggest improvements usually come from giving the model the right constraints.
My basic structure:
1. Context: What are we building, and for whom?
2. Objective: What specific outcome do we want?
3. Constraints: Budget, platform, format, tone, and limitations.
4. References: Examples of what good looks like.
5. Deliverable: The exact output we need.
6. Evaluation: What would make the result unacceptable?
For example, instead of asking for a marketing carousel, describe the target audience, the problem, the platform, the number of slides, the hook, and the action you want readers to take.
I've been applying this approach to our own AI workflows at BrandForge, especially when generating content intended for actual distribution.
What's one prompting technique that consistently improves your results?
The biggest improvements usually come from giving the model the right constraints.
My basic structure:
1. Context: What are we building, and for whom?
2. Objective: What specific outcome do we want?
3. Constraints: Budget, platform, format, tone, and limitations.
4. References: Examples of what good looks like.
5. Deliverable: The exact output we need.
6. Evaluation: What would make the result unacceptable?
For example, instead of asking for a marketing carousel, describe the target audience, the problem, the platform, the number of slides, the hook, and the action you want readers to take.
I've been applying this approach to our own AI workflows at BrandForge, especially when generating content intended for actual distribution.
What's one prompting technique that consistently improves your results?