Workflows give users control over how multiple prompts should be combined together at once.
Internally, this leads to more consistent prompting. Instead of team A directing AI to synthesize content in one format, then for team B to write their own prompt to generate an open-text response, workflows allow a single owner to define prompts for every step in a process in a consistent and centralized fashion. They might also define workflows as templates for others to re-use from as needed.
Generative workflows also give us more control over how our internal data flows across systems. Prompts that allow us to access data in third party systems can instruct the AI to retrieve specific data into the centralized workflow for further prompts to reference, summarize, or combine into a RAG-based response as references.
For example, take a workflow that synthesizes customer insights and builds draft knowledge center articles on a weekly basis using a string of prompts. In this scenario:
- A synthesis prompt analyzes customer insights and summarizes the key takeaways
- That summary can be run through ChatGPT to process it against a framework for severity and need
- The AI then generates a sample outline for the most severe knowledge base needs, and adds the remaining ideas to the backlog to keep pulse on
- Knowledge base managers are alerted by the AI when a new outline is ready to be reviewed
Even with generative AI, this process could take multiple hours to run and manage every week. Instead, AI can orchestrate the process in a matter of minutes. This type of process will be especially common as agents become more common as an AI Service.
Workflows aren't always the right tool for the job, but they are a powerful tool when leveraged.
