AI Product Management Series - Navigating the AI Product Lifecycle to Build Your First AI Product - Part 2 of 3

In my previous article, I focused on the fundamentals of AI in product management and discussed how Generative AI (Gen AI) is transforming the product development landscape. In this article, Let's explore into the key stages of the AI product lifecycle and examine how Gen AI is changing each stage. As we explore these topics, let's have some fun and build a practical application as part of our deep dive into AI product management.

A number of companies have already started using Gen AI in their applications. I find food delivery and grocery delivery to be particularly interesting use cases for Gen AI applications, probably because of my previous experience at Delivery Hero. Some of the delivery companies that have implemented Gen AI integration include the following:

• Doordash: Doordash is using generative AI to generate personalized recommendations for customers, optimize delivery routes, and predict demand.
• Instacart: Instacart is using generative AI to generate shopping lists for customers, identify and fix errors in product descriptions, and predict customer behavior.
• Zomato: Zomato is using generative AI to generate personalized recommendations for customers, optimize delivery routes, and predict demand.
• Blinkit: Blinkit is using generative AI to predict demand for groceries, optimize inventory levels, and plan delivery routes.

Not only grocery industry, but also several services across a variety of industries are using generative AI to improve user experience and develop new products and features. For example, Adobe is using generative AI to help creators express their ideas , Spotify is using it to personalize music recommendations, and Netflix recently used AI to assist in its creative process.

Now that we've seen how different companies are implementing it, it's time to explore how product managers can equip themselves to incorporate Gen AI into their products. In this article, I will focus on the product lifecycle aspect, while in the final installment, I'll concentrate on Large Language Models (LLMs), prompt engineering, and other technical facets to provide a comprehensive view.

Let's dive into understanding the product lifecycle of Gen AI products to gain deeper insights into how to proceed.

Role of Gen AI in AI Product Lifecyle

Generative AI (Gen AI) is making a significant impact on the AI product lifecycle. Gen AI models can be employed to enhance both the efficiency and effectiveness of each stage of the AI product lifecycle, from problem identification and ideation to model deployment and monitoring. The following table illustrates the specific ways in which Gen AI is transforming each stage of the AI product lifecycle.

So far, we've discussed the theoretical aspects of how Gen AI can transform each stage of the AI product lifecycle. Now, let's get hands-on and explore how you, as an AI Product Manager, can plan and execute a Gen AI product from scratch. To make this learning experience as accessible as possible, let's develop a very simple application.

We'll use a real-world example: a 'Terms & Conditions Analyzer' that leverages OpenAI's GPT-4 to simplify and highlight potential risks in terms and conditions documents. The knowledge and skills you'll acquire from this straightforward example will equip you to tackle more complex applications in the future.

Stages of Application Development - 'Terms & Conditions Analyzer'

Stage 1: Problem Identification and Ideation

• Planning: You've noticed that people rarely read Terms & Conditions before accepting them. This can lead to a lack of awareness about what they're consenting to, representing a real problem that needs solving.

• Gen AI Application: At this stage, you could use Gen AI to analyze social media complaints or survey data to quantify how big of a problem this is and what particular issues people are most concerned about.

Stage 2: Data Collection and Preparation

• Planning: You need a dataset of various Terms & Conditions texts and user-friendly summaries to train your model.
• Gen AI Application: Rather than manually collecting these, you could use Gen AI to scrape Terms & Conditions from various websites and then generate simplified summaries. This data will serve as your training set.

Stage 3: Model Training and Evaluation

• Planning: With your dataset ready, you need to train a model that can generate user-friendly summaries of Terms & Conditions documents.
• Gen AI Application: Use Gen AI to train this model, perhaps even employing it to generate synthetic data that can make your model more robust. Continually evaluate the model's performance to ensure it is learning correctly.

Stage 4: Model Deployment and Monitoring

• Planning: Once the model is trained, it needs to be deployed as a usable product.
• Gen AI Application: After deployment, use Gen AI tools to monitor its performance. Make sure it’s generating useful summaries and not missing any crucial red flags in the Terms & Conditions texts.

I have taken the liberty of creating the following technical architecture diagram. To explain it in simple terms, I will break the long (probably 10-page) terms and conditions into smaller text chunks. These will be fed in a loop to generate a summary, which will then be sent to GPT-4 to produce a final output.

Here's how you can set it up using Python

1. Clone the Repository:

git clone https://github.com/SanthoshSetty/terms-and-conditions-analyzer.git 

cd terms-and-conditions-analyzer        

1. Install Required Packages:

pip install -r requirements.txt        

1. Set Your OpenAI API Key:

export OPENAI_API_KEY='YOUR_API_KEY'        

1. Run the Flask App:

python app.py        

1. Usage: Open your browser and go to https://127.0.0.1:5000/. Paste the text of the Terms & Conditions and click "Analyze" to get a summary.

The code for both the back-end (app.py) and front-end (HTML) has been provided in the repository, so you can get the application up and running in no time.

Result

I tested Tiktok's terms and conditions against my code and the results are as follows.

Here is a summary of some important text that GPT-4 summarized and highlighted as problematic:

• Modifications to the terms and conditions without user consent could pose potential threats, as they might involve changes with which users are not comfortable.
• TikTok's right to use user content, even after account deletion, raises security concerns.
• Storing and processing user data for legal reasons opens up the potential for misuse of personal information.
• Although illegal activities and exploitation of minors are prohibited, enforcing this could be challenging, and there might be loopholes that predators could exploit.

Wrapping Up and What's Next

As we've journeyed through the second installment of this three-part series, we've focused intently on the AI product lifecycle and the transformative impact of Generative AI. We didn't just stick to theories; we got practical by planning, building, and deploying a Gen AI-based product—our Terms & Conditions Analyzer. This hands-on example showed that leveraging the power of Gen AI doesn't necessitate a Ph.D. in computer science; a deep understanding of customer needs and a willingness to experiment are often sufficient.

In the next and final installment of this series, we'll dive deeper into some advanced topics crucial for AI Product Managers. We'll explore prompt engineering, a technique that can fine-tune your AI model's performance. We'll also examine Large Language Models (LLMs) and discuss how they differ from other AI models in terms of capability and application. Most importantly, we'll address ethical considerations in AI product management to ensure that your products are not only innovative but also responsible and fair.

So stay tuned—you won't want to miss the final chapter of this series, where we'll weave all these elements together into a comprehensive guide for AI Product Management in the Generative AI era.