Enterprises Need a New Toolbox to Tune up Their GenAI Development
- By Jihad Dannawi, DataStax
- March 21, 2024
Like F1 competitors, no detail is too small to investigate for organizations racing to adopt generative artificial intelligence (AI). From engine design to the material used in car bodies and wheels to the weight and reflexes of the drivers, race car teams meticulously explore every detail to beat their opponents to the finish line.
In the same way, enterprises must seize every opportunity to outrace the competition when adopting generative AI. After ChatGPT was launched, the seemingly endless possibilities with which generative AI can transform how organizations operate and do business have captured the imagination of decision-makers.
As companies embrace generative AI, they should gear up for new challenges and the amplification of existing ones. For example, despite Singapore's status as a regional leader in AI adoption, its National AI Strategy 2.0 highlighted that existing AI talent and resources are limited to a few companies. In other words, the distribution of AI expertise and resources is not evenly spread but rather concentrated in specific areas, posing a potential challenge for the broader adoption and development of AI technologies.
One way organizations can alleviate these challenges is to ensure their existing AI talent has the tools available to simplify their work processes while providing maximum performance from the generative AI applications that they are developing.
Vector capability is the engine of generative AI adoption
For developers working on generative AI, vector database capabilities are crucial in delivering the expected accuracy and reliability of query responses. By enabling retrieval-augmented generation (RAG), large language models (LLMs) can tap massive datasets to generate highly accurate and personalized results. When vector capabilities were introduced in Datastax's Astra DB in July 2023, approximately half of all new users said they used it to build generative AI applications.
Further highlighting the importance of vector database capabilities is the race by database providers to improve the capacities mentioned above of their respective offerings. For example, Astra DB incorporated the JVector search engine to significantly improve its retrieval performance, particularly regarding the relevance of its results.
Translating power into performance with an API
A stumbling block for app developers looking to utilize powerful vector search capabilities is working with Cassandra Query Language (CQL), which requires more data modeling knowledge from developers than they felt was necessary for simple rack applications. Instead, if one were to enable users to utilize Python and Javascript when using particular database platforms, such as through an interface in the two languages described above, more developers could leverage vector search capabilities for their apps.
A good example arises with the recent introduction of a new Data API by DataStax. It simplifies the AI development experience and integrations with a host of major GenAI ecosystem leaders, such as LangChain, LLamaIndex, OpenAI, Vercel, Google Vertex AI, Amazon Bedrock, GitHub Copilot, Azure, and all major platforms.
Ensuring first place in the generative AI race
Most races do not involve running a straight line, and the competition to take advantage of generative AI is no different. It is not enough to be the first to develop a generative AI app to develop a competitive advantage. Businesses must also ensure that their new offerings perform exactly as advertised, and to do so, their generative AI models must be able to generate the most accurate responses to queries in the shortest possible time.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/VectorMine
Jihad Dannawi, DataStax
Jihad Dannawi is the APAC general manager at DataStax.