Part 1: Identify your use case and deconstruct the scenario to understand what tools you need for building an Gen AI application

Let’s begin this first part with a few definitions and introductions. I understand it may seem repetitive, and if you find it lengthy or uninteresting, simply scroll down until you see the phrase ‘Can AI do this?’.

What is Generative AI?

Generative AI is a form of artificial intelligence capable of creating new content and ideas, including conversations, stories, images, and videos. It is supported by large models pre-trained on extensive data sets.

By leveraging Generative AI, now you can redesign your applications, create innovative customer experiences, obtain exceptional productivity levels, and revolutionize your business.

Generative AI tools utilize sophisticated algorithms to analyze data and produce unique insights, enhancing decision-making and streamlining operations.

This technology enables computers to generate unique content by recognizing and applying basic patterns from the input data.

How does Generative AI Work?

Generative AI models employ neural networks to recognize the patterns and structures in existing data, thereby creating new and original content.

Generative AI models have significantly advanced by using various learning methods, such as unsupervised or semi-supervised learning, for training. This has enabled organizations and enterprises to use vast amounts of anonymous data for the swift and straightforward creation of baseline models.

As the term suggests very clearly, these base models serve as the foundation for AI systems that perform multiple tasks.

What are the Applications of Generative AI?

Generative AI applications can facilitate the creation of realistic animations, text, and images in minutes. They offer benefits to various industries such as surveillance, healthcare, marketing, advertising, education, gaming, media, podcasting, and more. Here are a few examples:

Generative AI has drawn significant interest due to its potential, captivating tech enthusiasts, industry leaders, investors, and the broader public. Current market sentiments include:

1. Overwhelming Enthusiasm: With major tech companies heavily investing in Generative AI and its applications, optimism abounds. Success stories related to creative content generation further stoke this enthusiasm.
2. Investor Expectation: The surge of funding into AI startups signals robust investor faith in the technology’s long-term feasibility and profitability.
3. Consumer Curiosity: While the average person may not fully understand the complexities of Generative AI, they recognize its transformative potential in areas such as content creation, gaming, and personalized user experiences.

Myths about Generative AI:

Despite the potential of Generative AI, there are several misconceptions about its capabilities:

1. Universal Application: A common myth is that Generative AI can be universally applied across all sectors with the same level of effectiveness. In fact, its usefulness depends on specific requirements and conditions.
2. Replacement of Human Creativity: While Generative AI has demonstrated its ability to create art, music, and prose, it cannot fully replace human creativity. The depth and breadth of human emotion and ingenuity can be complemented, but not duplicated, by AI.
3. Immediate Large-Scale Transformations: Many people believe that Generative AI will immediately revolutionize industries. However, integrating such technologies is a gradual process that requires substantial adjustment and refinement.

Reality about Generative AI:

Generative AI has unlocked a myriad of possibilities and real-world applications. The power of this advanced technology has paved the way for several use cases that are reshaping the way we interact with the world around us:

• Classify information based on visual or textual data.
• Rapidly analyze and adjust strategies, plans, and resource allocations using real-time data.
• Create content in various formats automatically to enable faster response times.
• Summarize large amounts of data to extract key insights and trends.
• Assist in quickly retrieving pertinent information and providing immediate responses via voice or text.

Can AI do this?

As you may already know, using Gen AI allows you to generate new content, such as text, audio, and video. There are tons of use cases that can be implemented, but identifying suitable AI use cases for your product requires careful discovery, strong product and design skills, and ongoing exploration and experimentation.

Identifying use cases for generative AI is not a simple task. It involves extensive groundwork unrelated to AI, such as reassessing and sometimes even defining objectives, conducting research, and liaising with stakeholders to understand business problems in depth. This process may not be enjoyable, as most prefer the creative aspect of building things. However, even if your company is established and operating smoothly, it’s likely that your Customer Journey Map (CJM) is outdated and there’s a lack of a comprehensive understanding of how the business functions.

The process of identifying an AI use case typically involves the following steps:

• Define the problem and the goal.
• Understand the current situation.
• Envision the desired future scenario.
• Identify potential AI use cases.

Once you’ve completed the meticulous research and discovery process, that’s when the intriguing part starts. However, there’s no magical algorithm to pinpoint a specific Generative AI use case for your issue. To connect the dots, you merely need to acquaint yourself with Generative AI’s capabilities by studying the field, staying updated with the latest news, and experimenting with new products. (I personally read at least 5–10 articles and/or papers every day about new generative AI models or methods on how to use them.)

Here, you can find one of the most comprehensive lists of Generative AI use cases for inspiration. ✌️

My receipt:

After I already identify a task and its associated pain point, I personally follow these steps before considering implementing a specific use case with Generative AI:

1. Does make sense to use AI for this?
2. Can AI perform this task effectively?
3. Can it be done without AI, in a simpler way?
4. What benefits does the end user gain by implementing this task with AI?
5. Do I have enough context data (from the user or the system) for this task to succeed?

As a fan of rapid prototyping, I first break down the task into smaller tasks before writing the actual code for the agent. After identifying the required input data and desired output data, I can test the prompts directly in the popular ChatGPT. You can quickly receive actionable tasks or determine the feasibility of your use case.

Closing notes

In every use-case you implement in your application, the primary consideration should always be your end-user — the human being. It is essential to keep humans in the loop at all times. Despite the advanced capabilities of AI, the human user should retain control, particularly in making critical decisions.

Even as AI provides an invaluable level of support, expertise, and backup, remember that it serves as a tool to augment human abilities, not replace them. It’s the human users who provide the unique context and understanding that guide the use of technology in meaningful ways.

So, while you leverage the transformative potential of Generative AI, ensure that your implementation strategies are still grounded in the human experience. This human-centric approach will not only make your application more effective but also more relatable and useful for your audience.

Do you have a specific use case in mind that we can quickly prototype in the upcoming parts of this article series?