Use Case 1: Code Generation
One important application of Generative AI involves using large language models (LLMs) to create code snippets and simplify the software development process efficiently. Generative AI in software testing, Code generation with AI, and Generative AI in Data Analytics, even though we don’t view Generative AI as a replacement for crafted and deliberate code writing practices, the efficiency improvement it provides by automatically generating template codes—or accessing databases of ready made codes customized for specific situations—can greatly accelerate project delivery when utilized appropriately.
One important application of text-based large language models is their capability to assist in transitioning legacy code to platforms efficiently by serving as aids during these shifts, in technology platforms. For instance, one common scenario is when transitioning analytics from Qlik Sense reports to Power BI reports. During this process a significant aspect involves transforming Qlik’s syntax into DAX code for the reporting interface. Traditionally this task necessitates someone, in both software tools.. Generative AI has the capability to automate the transformation of Qlik syntax into DAX efficiently hastening the delivery of solutions, as a whole.
Use Case 2: Virtual agents and chatbots
If you’re thinking about incorporating a chatbot on your website to improve customer support services nowadays due, to the advancement of (LLMs) it has become much simpler to implement and launch chatbots than in the thanks to recent developments in technology and AI capabilities. By integrating chatbots with front end analytics tools you can add a layer of context, to your reports which enhances the insights provided by data and gives depth to the analysis.
Cloud services are rapidly improving their ability to process “data-in” efficiently and offer options for launching advanced search functions and pre built extensive large language models tailored to your specific data sets. The accessibility threshold is decreasing as a result.Chatbots can be seamlessly incorporated into work processes via API access points or integrated into applications based on the selected cloud provider. In addition to that similar approaches can be applied when utilizing open source platforms, like LangChain.
Use Case 3: Dashboard layouts and visualizations are automatically generated
Creating representations and graphical components can be done using auto suggested prompts supported by the BI features of a business intelligence tools built in AI functionality. These AI powered tools can also assist in structuring the appearance of a report while ensuring an appealing visual design.
This method provides a time saving benefit when designing dashboards or improving current visualizations that may have lost their user friendliness over time.
Use Case 4: Workflows, Automation and Applications
Simple software interfaces, like Zapier, Power apps and Power Automate can make use of API requests to connect with GPT and similar advanced large language models (LLMs). For example, you could teach GPT to function as a customer support representative or to scan a business network, for data that can be shared through a form based app.
One can utilize these instruments for creating automations, such as crafting automated email responses, for incoming queries or generating trigger based prompts to compose automated email replies seamlessly.
Cautions for Generative AI
Although generative AI has a lot of promise, you should consider the following risks before using generative AI into your data strategy:
- Basis of Evidence: Generative AI makes use of large language models (LLMs) as well as neural networks that explore numerous possibilities to generate outcomes.This poses a challenge when it comes to elucidating the rationale, behind code implementations or design choices made throughout the entire procedure.
- Security, IP, and PII Risks to Data: One of the benefits of Generative AI is how easy it is to use for users.Yet this ease of use also comes with a downside. In the context of generative AI in cybersecurity, there’s a chance that training data could accidentally contain sensitive information if proper precautions aren’t taken.
- Accuracy: In the public domain realm of LLMs such, as ChatGPT utilize data sourced openly from platforms and sources available on the internet; however in the sphere of private organizations how well these LLMs perform hinges solely on the caliber of training data or metadata supplied to them If the quality of input data is subpar it may have adverse impacts, on the precision of outputs produced by the model.
- Cost: While its easier to get started than before, in this field of work or study or business the potential downside of overspending is higher than ever. Setting up search capabilities with a language model demands a lot of computing power. Before you decide to incorporate these models into your day, to day operations it’s crucial to grasp the impact of implementing and maintaining them.
Alternatives in Technology for Generative AI
Generative AI capabilities are available in most popular analytics tools in various forms. The tools you could use for Generative AI are listed here, along with an example of how they are typically used.
Microsoft
- The Azure OpenAI Service provides access to AI models that can generate content creatively and effectively engage users in various contexts and industries. This service offers a range of trained models and allows users to tailor custom AI solutions to meet their specific needs. Additionally, it offers pricing options based on tokens and images usage to cater for user requirements.
- Users have the ability to utilize Copilot to design representations of data and extract insights, for reports using Power BI by crafting narrative summaries and generating DAX expressions.
Qlik
- Qlik offers connectors for OpenAI integration, in their platform Qlik Sense. Such as the OpenAI Analytics Connector for adding content to front end applications and the OpenAI Connector, for Application Automation that helps developers enhance their workflows with expressions and scripts. AI Text End
- Vertex AI allows developers to customize and integrate models into their applications easily using the Generative AI Studio, on Vertex AI platform.
- The Generative AI Application Builder provides a to use platform, for developers to craft and launch chatbots and search applications.
AWS
- AWS Bedrock is a service provided by AWS that gives users access to third party language models (LLMs) and base models, from Amazon to help create and implement generative AI applications easily.
Tableau
- Tableau Pulse is powered by Tableau GPT. Harnesses the features of Einstein GPT to provide automated analytics and deliver insights, in both language and visual representations.
Sigma
- Sigma AI offers a variety of AI powered features embedded in the platform such as Input Tables AI for generating input tables and Natural Language Workbooks that help in creating workbook elements within Sigma software platform. Moreover, users can also benefit from Helpbot a chatbot designed to aid users by organizing and indexing all help and community articles within Sigma software.
LangChain
LangChain is a framework, for developers working with large language models (LLMs) aimed at connecting them with external elements to enable the development of LLM driven applications effortlessly It enables developers to create applications that make use of LLMs from platforms like OpenAI and Hugging Face in combination, with different data sources.
Advice for Using Generative AI Successfully
Recognize how open platforms for generative AI work.
Get familiar with how open Generative AI platforms operate to understand what they can do.Learn the art of crafting prompts and figuring out the amount of information required for best outcomes.The starting point for exploring tools, like Chat GPT is quite user friendly.
Establish master prompting as the norm
Effective communication is crucial when using AI technology in your organization to ensure content creation across all channels. Creating guidelines, for communication is key. Defining your organization’s identity and setting the desired tone for interactions with the language model are steps. By following these guidelines throughout your communications strategy and content creation process using AI technology helps maintain a voice and tone, in all company wide communications.
Recognise the cost structure
Using Generative AI for single use scenarios is cheaper than deploying it across an organization due to costs involved in scaling up operations significantly. To prevent expenses during the implementation of a solution in real world settings, it is vital to have a well defined strategy for optimizing costs from the start. Its recommended to keep an eye on spending and usage patterns while the projects in progress. Moreover, restricting access to development resources at the beginning, can aid in controlling research and development activities within your company.
Establish a solid data strategy.
To effectively utilize AI technology in your operations it is crucial to have an understanding of where your data’s stored, how it is maintained and its organizational structure. Moreover incorporating a data governance plan is vital, for the success of your data strategy.
Create a compelling use case
Generative AI acts as a tool, with practical uses at present times. Think about the core operations in your company. See how Generative AI can improve automated workflows.
Give data privacy top priority
Ensure that precise data is inputted into your Language Model Models (LLMs) in compliance with the protocols. Implement measures such as turning off chat history and training settings in ChatGPT as needed. When expanding your development initiatives with leading cloud platform services it is crucial to grasp elements, like data storage guidelines and the ultimate destination of your data when converting prompts into replies.
Keep adding to the quality of your data and related information
To make the most of LLMs in a business setting requires meeting requirements. Utilizing open source LLMs such as ChatGPT, LLaMA and Bard can be appealing due to their transformer models drawing from data available online. Henceforth the quality and extent of your data will significantly impact the level of engagement to that offered by open source LLMs.
Select from LLMs that are general and domain-specific
Which option is most suitable, for your requirements; Domain language models such as BloombergGPT are trained using data customized for industries and applications, in contrast general purpose language models may face challenges comprehending the specialized jargon utilized in your queries.
Recognise the capabilities of your cloud platform
Cloud services such, as AWS, Microsoft and Google are in a race to simplify language model (LLM) building and application for an user base. Each provider has its way of combining document storage vector databases, embedding models, LLMs and cognitive search for responses to user queries. Understanding the tools and resources is crucial, for launching chat services and creative AI applications. It’s also crucial to grasp how the pricing works on the cloud platform you’ve selected as you scale up these solutions for an user group.
Examine the connection between BI and generative AI
Large language models mark a change in how we understand and use data going forward. Possibly in the future we won’t rely much on reports and dashboards but will instead use organized cues to easily uncover insights while a central data hub handles the gathering and combining of information behind the scenes. On the hand there might be a scenario where standard Business Intelligence tools work alongside Generative AI in a way forming a mixed method, for analyzing data. In whatever way this evolution progresses forward in the changes occur; it is essential, for stakeholders. End users to evaluate how Generative AI might improve or potentially substitute their existing BI approaches moving ahead.
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