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Over the past few months, generative AI has become a valuable partner for startups looking to quickly harness the power of AI in their domain. Generative AI offers a range of products and services that enable startups to take advantage of capabilities such as natural language processing, computer vision, and generative design.
This blog post explores three common startup use cases leveraging Azure OpenAI Service, the leading generative AI model for startups. Azure OpenAI services are available directly for both OpenAI credits and access to Azure OpenAI APIs as a benefit of the Microsoft for Startups Founders Hub program. By adopting these use cases, startups can improve customer service, content creation, marketing, data analytics, product functionality, security, quality control, user experience, product development, prototyping, testing, and optimization. While there are many avenues startups can explore using the Azure OpenAI Service, it’s a great starting point for piloting this technology with existing SaaS offerings and learning how best to incorporate it into existing applications. There are some key use cases to focus on.
Use Case 1: Natural Language Processing (NLP)
Perhaps the most common use case for many members of the Founders Hub Program is Natural Language Processing (NLP). This is the field of AI that deals with understanding and generating natural language such as text and speech. NLP helps startups improve customer service, content creation, marketing, and data analytics. For example, GPT-4 in Azure OpenAI Service is a deep learning system that can generate consistent and relevant text based on specific prompts. Startups can use GPT-4 to create chatbots, product descriptions, email campaigns, and abstracts. It can also answer questions, perform calculations, and provide recommendations based on natural language queries.
Use Case 2: Hyper-personalization
Another interesting use case is leveraging OpenAI for application hyper-personalization to improve user engagement. This area of AI is often referred to as generative design because it is used to create novel and optimal designs based on user profiles, criteria, and constraints. Generative design helps startups innovate in product development, prototyping, testing, and optimization. For example, DALL-E in Azure OpenAI Service is a deep learning system that can generate realistic and diverse images based on natural language input and data classification that can differ at the user level. This approach allows startups to use his DALL-E to build personalized designs based on natural language commands and collected user data. It can be used to generate logos, icons, illustrations, mockups, and product pages, and can also manipulate existing images to reflect personalization needs and real-time user signals.
Use Case 3: Unstructured Data
But perhaps the most interesting use case for this technology, which is fast becoming a best practice for startups in many industries and verticals, is the ability to reason over vast amounts of unstructured data. Such data was previously largely inaccessible to most startups due to its high complexity and lack of dedicated data scientist resources (especially for early-stage startups). Inference on data is the ability to use natural language or code to extract insights, patterns, and knowledge from large and complex data sets. It helps startups solve problems, make decisions, and create value from data. For example, a startup that wants to analyze customer feedback can use his GPT-4 model in Azure OpenAI Service to generate summaries, sentiment analysis and recommendations based on feedback. You can also use the GPT-4 model with the capabilities of Azure OpenAI services, such as speech-to-text and Form Recognizer, to find specific data points from different types of unstructured data and convert them to structured format. You can also. That structured data can be easily analyzed for insights using tools like Power BI.
Drill down for use case 3: Convert unstructured data to structured format
Transforming unstructured data into structured data using Azure OpenAI Service GPT-4 models requires a few simple steps.
- Define input and output formats. Specify the kind of unstructured data to convert and the kind of structured data to retrieve. For example, you may want to convert a text document into a table or spreadsheet.
- Please give some examples. Provide an example of what the input and output might look like. For example, you can provide sample text documents and corresponding tables or spreadsheets that demonstrate how to extract and organize data. The more examples you provide, the better your model can learn from them and generalize to new inputs.
- Fine tune the model. Fine-tune a sample OpenAI GPT-4 model with a suitable learning algorithm and hyperparameters. This allows you to adapt your model to specific tasks and domains and improve its performance.
- produce output. Feed unstructured data into a fine-tuned OpenAI GPT-4 model to generate structured data in the desired format. Post-processing of the output may be required to ensure the quality and accuracy of the output.
As an example, let’s use the following prompt, based on a hypothetical call center interaction converted to text using Azure’s Text-to-Speech API:
Converts call transcripts to JSON format with first name, last name, and call reason fields.
Hello, this is Alice from XYZ company. How can I help you?
Hello Alice, my name is Bob Jones. I’ve been following your startup for a while and am using the free version you just shared.I recently reached the threshold to use this app for free so I contacted them to create a paid subscription
“reason_for_calling”: “Create a paid subscription”
Generative AI is already bringing value to startups
The above use cases have emerged as generative AI best practices among Founder Hub members of Microsoft for Startups. There are many areas in this technology, but leveraging natural language understanding and generation capabilities to infer the structure and meaning of unstructured data is a common one for startups because it spans a wide variety of applications and solutions. Starting point. By leveraging generative AI models across their applications, early-stage companies with an early adopter mindset can quickly adopt innovation, gain a competitive edge, and unlock new engagement models.
Not part of the Microsoft for Startups program? Sign up for the Founders Hub today. With instant access to the Azure OpenAI service, you can start experimenting with this technology. You will be amazed at the comprehensiveness of the model, its capabilities, and its ease of use in many areas of your startup.
Tags: generative AI, hyper-personalization, Microsoft for Startups Founders Hub, natural language processing, OpenAI, unstructured data