"Digitale Wissensbissen": Generative AI in Business-Critical Processes
Generative AI in business-critical processes
In the last episode of our podcast, we took a close look at the challenges and limitations of generative AI. We talked about hallucinations, alignment issues, costs and performance – all the factors that could currently complicate the integration of AI into business-critical processes. But the exciting question remains: can generative AI be used in business-critical processes despite these hurdles? And if so, how?
Key Points from the Episode
- “Generative AI should be based on human artifacts, not replace them.” – It is important that AI applications build on human-generated content. A code of conduct, for example, should be created by a human before the AI generates further content from it.
- “Prompts are closer to programming than to communication.”
- Creating prompts for generative AI requires a precise and rule-based approach, similar to programming. End users should therefore not create prompts directly to ensure the quality of the results. - “A human-in-the-loop approach is often optimal.”
– In mission-critical processes, a human should always be in control to ensure the quality and consistency of the results and to enable continuous improvements. - "Retrieval Augmented Generation (RAG) is now state of the art."
– Information should be stored in a vector database and retrieved by semantic similarity before being processed by a Large Language Model - “Data preparation is key to success.”
- Careful preparation of data, such as OCR processing of PDF documents, is crucial for the quality of AI results.
Summary
In this episode, we highlighted the challenges and opportunities of integrating generative AI into business-critical processes. Two main rules were emphasized: First, AI should be based on human-generated content and not replace it. Second, prompts are more like programming and should not be created directly by end users. A human-in-the-loop approach, where a human retains control, is often optimal to ensure the quality and consistency of the results.
A specific example from the area of compliance shows how these principles can be applied in practice. By using retrieval augmented generation (RAG) and careful data preparation, compliance questions can be answered efficiently and consistently. This approach can also be applied to other business-critical processes such as tender management.
Generative AI has the potential to transform business-critical processes when used correctly. Adhering to the principles mentioned above can help to minimize risk and maximize benefits.
(Episode in German only)
