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GenerativeAIhas huge potential to revolutionize business, create new opportunities and makeemployeesmore efficient in how they work.
All these signals show that generative AI is growing in use.
Head of Developer Relations at DataStax.
Where are the gaps?
So what are the challenges that exist around generative AI?
The first of these is around how to get data ready for generative AI systems.
The second is how to integrate these systems together and how to develop software around generative AI capabilities.
For many companies, generative AI is inextricably linked to large language models (LLMs) and services likeChatGPT.
For simple queries, aChatGPTresponse can be adequate.
But for businesses, this level of general knowledge is not enough.
To solve this problem, techniques like Retrieval Augmented Generation are needed (RAG).
There are several approaches possible here, from looking at individual words through to sentences or paragraphs.
This is where there can be challenges for developers.
Generative AI is most associated with Python, the software language used by data scientists when building data pipelines.
Each of these steps will involve multiple components working together to fulfil a request.
This integration work will be a significant overhead if we cannot abstract this away using APIs.
By using standardized APIs instead, the job will be easier for developers to manage over time.
This also makes it easier to integrate generative AI systems into front-end developer frameworks like React and Vercel.
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