Welcome to the 12 Days of AI Use Cases!
In this series, we’ll be looking at different use cases for AI – in particular, generative AI and large language models, the software that powers tools like ChatGPT, Google Gemini, and Anthropic Claude. Each day, we’ll look at the use case through the lens of the Trust Insights 5P Framework to see the role AI plays in achieving real, tangible outcomes.
These use cases are designed not only to be read, but also to be given to the generative AI tool as part of a prompt to help you achieve the outcomes you’re after. Ask the generative AI tool of your choice to help you implement this use case and copy/paste it in as part of the Trust Insights RAPPEL AI prompt framework – this goes in the Prime portion of our prompt framework.
Let’s dig in!
Purpose
Generative AI transforms the code development process by dramatically reducing development time from weeks to hours while enabling non-technical ideators to bring their concepts to life. This solution addresses the fundamental challenge of slow, resource-intensive code development by leveraging AI’s deterministic advantages in code generation, resulting in faster deployment and customized solutions that precisely match business needs.
People
The Ideator serves as the primary actor, originating the concepts and requirements for code development. They drive the initial vision and define the desired outcomes, regardless of their technical expertise level. The ideator collaborates directly with the AI system to translate concepts into functional code.
Technical stakeholders include developers and coders who validate AI-generated code, ensure quality standards, and provide expertise in prompt engineering. These stakeholders critically evaluate the AI’s output, identifying both successful generations and potential errors, maintaining the crucial 100% accuracy requirement for functional code.
Process
- Define clear project requirements and desired outcomes
- Select appropriate programming language based on project needs and AI model capabilities
- Generate initial code through AI model interaction
- Review generated code with technical stakeholders
- Test code functionality against requirements
- Debug and optimize code through iterative AI assistance
- Deploy validated code to production environment
- Establish maintenance protocols for ongoing updates
Platform
- Advanced Generative AI models (Claude 3.5 Sonnet, Google Gemini 2.0)
- Popular programming languages (Python, Ruby, PHP)
- Development environment compatible with chosen language
- Version control system for code management
- Testing frameworks for quality assurance
- Deployment tools appropriate to application type
Performance
The primary goal focuses on development time reduction, transforming weeks-long development cycles into hours-long processes. Success manifests through functional, deployed code that meets all specified requirements while achieving significant time savings compared to traditional development methods.
Key performance indicators track three critical metrics: development time reduction (comparing AI-assisted vs. traditional development times), cost savings (measuring reduced development resources and potential subscription eliminations), and customization effectiveness (evaluating how well the solution meets specific business requirements compared to off-the-shelf alternatives).
We hope this use case is clear and helpful. If you’d like help implementing it or any other AI use case, reach out and let us know.
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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.