Generative AI vs LLMs: definitions, differences and everything else businesses should know

June 18, 2024

FAQs

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A: AI engineers can specialise in any type of AI; the focus isn’t generally on the ‘type’ or ‘subset’ of AI technology that will be used for an AI-powered effort, but what the objectives are, and how these can be attained. Therefore, comprehensively assessing your business’s existing needs and challenges is highly essential, as this shall help your team of AI engineers determine which AI tools and technologies are most suitable to meet organisation objectives.
A: Generative AI uses AI technologies (as its name suggests) to ‘generate’ new content - while predictive AI uses any AI-based technology to identify and forecast trends based on historical data. In other words, predictive AI will not generate any brand new content, except for the predictions it makes based on all the data it currently has.

Predictive AI is a big use case for discriminative AI, as a result; discriminative AI, unlike its generative AI counterpart, understands boundaries and only classifies data based on what it knows - as opposed to making assumptions from data and creating something brand new.
A: Albeit being a breakthrough in the history of AI technologies, generative AI also has its share of challenges, which mainly include:

● Hallucinations: this involves generative AI tools fabricating patterns, and subsequently generating information that’s false, or completely irrelevant,

● Risk of data leaks: Confidential data can be inadvertently leaked as generative AI models ingest data and train from prompts, to subsequently output it during a prompt made by someone else,

● Model manipulation: Generative AI models can be manipulated to deliver false or biased information, with repeated prompting.

However, partnering with generative AI companies can enable organisations to deliver custom models that can help mitigate these limitations, as models can be built to circumvent said limitations, as well as deploy protections which prevent data leaks from occurring.

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