How to Choose Fit for Purpose Generative AI Solutions That Will Grow Your Business
When generative AI technology first emerged, there were instances where professionals used open-source tools for business purposes, neglecting their responsibility to exercise due diligence and relying solely on these powerful language engines. More recently, there have been plagiarism cases brought before the courts, further highlighting the potential drawbacks of this creative technology. This has led to scepticism in the business world about the implications of putting such tools in the hands of employees, despite the undeniable productivity gains. Concerns about brand reputation, credibility, consumer trust, and performance have understandably caused some hesitation in adopting this powerful technology.
So, how can businesses responsibly harness the productivity and performance benefits of generative AI without facing these consequences? Here are some factors to consider when selecting a generative AI solution that is suitable for business:
Security is key
Ensure that the infrastructure security meets all your standard requirements, including necessary hosting, accreditations, and certifications.
Protect your content
Make sure that your intellectual property is not being used to feed open-source large language models (LLMs). It is common for software companies to enter into enterprise agreements with LLM owners to power their generative AI products. It is crucial to have a Zero Data Retention agreement in place to secure both the inputs and outputs of the system and prevent them from being used to feed the LLM. Additionally, it may be beneficial to know if multiple LLMs are used to ensure reliability and longevity.
Relevant inputs for relevant outputs
Verify that the text generation is relevant to your organisation. Look for a solution that allows access to your company’s knowledge base to generate customised content. This can be considered an additional feature that enhances the reliability of the generated content using the retrieval-augmented generation (RAG) method.
Generated text is backed by evidence
Ensure that the software generated text includes proper r
