Implementing Generative AI in Your Product

Exploring goals, strategies, and real-world examples of how to use generative AI in your product

Introduction

Embracing the rapidly evolving field of artificial intelligence (AI), generative AI has proven to be a transformative force. Its potential goes beyond mere trends, as integrating AI capabilities into your products has become essential. However, with generative AI being relatively new and full of untapped possibilities, it can be challenging to determine where to begin. In this blog, we will provide insights on how to approach generative AI, offering practical examples from Rocketium's implementation plans. These real-world scenarios will serve as inspiration for incorporating generative AI into your own product. It is assumed that you have a basic understanding of generative AI concepts and terminology.

Choosing Your High-Level Goals

Start by assessing which of these overarching objectives align with your business, enabling you to focus on specific use cases where Generative AI can be applied effectively.

1. Creativity and Innovation: Generative AI can break the mold, fostering the generation of novel ideas, designs, and content.

2. Personalization and Customization: Offering customized services and products enhances user satisfaction. Generative AI can curate experiences tailored to individual users, understanding user preferences on a granular level.

3. Efficiency and Automation: Generative AI can automate various tasks, reducing manual effort, and increasing efficiency across several domains such as content creation, product design, and manufacturing.

4. Enhanced User Experiences: Generative AI can create immersive, interactive, and engaging content to enrich user experiences.

5. Problem Solving and Optimization: Generative AI can aid in resource allocation, decision-making, and process optimization by analyzing extensive data and generating potential solutions.

6. Data Generation and Augmentation: Generative AI can generate synthetic data to supplement existing datasets, improving machine learning models' performance.

Exploring the Domains of Generative AI

After determining your goals, you can explore generative AI within the following contexts, which are particularly relevant in the near future. In the next section, we will delve into each area and discuss our thoughts and strategies for implementation.

•  Text -> Text

  • Text-to-text transformations involve AI models like GPT-4 that predict the next word in a sequence based on the preceding words, enabling generation of contextually related sentences and paragraphs. Examples include machine translation and text summarization.

• Text -> Image

  • Text-to-image generation involves creating visual representations from textual descriptions using models like GANs. A well-known example is DALL-E, which generates images from textual prompts. Applications include art, design, and virtual reality.  

• Image -> Text

  • Image-to-text conversions employ transformer models for generating text. These AI models craft textual descriptions for images, significantly enhancing the capability to semantically search for an image via text. Refer https://minigpt-4.github.io/

• Image -> Image

  • Image-to-image transformations modify an input image to generate a new output image using Generative Adversarial Networks (GANs). Examples include generating variations of an image, upscaling an image, removing part of an image, and extending an image,… Applications are wide, ranging from art and entertainment to autonomous driving and medical imaging.

Once you have chosen a specific area to focus on, you can start by exploring existing stock solutions available in that domain. It is recommended to begin with these stock solutions as a starting point. However, the true value of generative AI is realized when you are able to generate brand-compliant content that aligns with your company's language, tone, and voice. To achieve this, you will need to delve into the process of fine-tuning the AI models, ensuring they are customized to meet your specific requirements.

Finetuning, Embeddings, or Both?

To achieve brand compliance and generate content that aligns with your company's language and tone, fine-tuning and embedding techniques are crucial. Based on experiments conducted, both approaches have yielded similar results. However, fine-tuning is recommended when you have specific data sets that differ from the general set while embedding and feeding context to a language model has produced the best results for most use cases. There are a lot of articles written on this topic. So we will elaborate more on this. Below are some great reference materials that can help you with fine-tuning and embedding.

• Create embeddings

• How to finetune chatgpt

• Prompt engineering

Implementing Generative AI in Rocketium's Creative Operations

Here are a series of initiatives we have slated to incorporate Generative AI. More importantly, these should serve as an example to draw from and inspiration to incorporate into your own product development.

1. Brand-Compliant Creative Copy Generation: Advanced language models (LLMs) like GPT-4 can be trained on a company's past ad copies, which can be vectorized and stored in databases such as Elasticsearch. When a new copy is needed, a query can be run to fetch the most similar past copy vectors, and this copy can be used as a context for the model to generate a new, brand-compliant copy. The success of this process highly depends on precise prompt engineering, ensuring you communicate effectively with the AI.

2. Design Compliance: Using Generative Adversarial Networks (GANs), AI models can be trained on past ad designs. GANs are designed to create new data with the same statistics as the training set. Hence, they can generate new, brand-compliant designs that are similar to previous advertisements but carry a freshness in their aesthetics.

3. Media Library Insights: Generative AI can be used to extract metadata from media assets (using CNNs for images and LLMs for any associated text), which can then be vectorized and stored for semantic search. This is a significant improvement over conventional methods which rely only on text metadata and exact matching algorithms.

4. Image Utility Tools: AI offers a variety of tools for image manipulation, such as super-resolution (upscaling an image without losing detail, often achieved through models like SRGAN), background extension (using texture synthesis or inpainting techniques), and object removal (using convolutional neural networks that can be trained to recognize and erase certain image parts).

5. Improved Project Briefs: AI can use Named Entity Recognition (NER) to identify if crucial elements (like audience, objectives, channel, tone) are missing in project briefs. Additionally, LLMs like GPT-4 can be used to rewrite the brief in a format preferred by the company, maintaining brand consistency and clarity of communication.

6. Mood Board Generation: Models like GANs, trained on various aesthetic styles and visual elements, can generate images that fit a specific mood or theme. These AI-generated images can be dynamically added to a mood board, aiding in the creative brainstorming process for ad campaigns.

7. Stock Content Generation: AI can also generate brand-consistent stock content using GANs. By training on a diverse range of stock images, AI can generate new, unique variations of images that can be used in advertising material.

8. Creative Compliance: Generative AI can help improve creative compliance by identifying non-compliant elements. Using a combination of Computer Vision and Natural Language Processing (NLP) techniques, creative content can be deconstructed into its constituting elements (like text, image, color schemes, logos) and each element can be checked for compliance with brand guidelines.

9. Creative Analytics: By deconstructing ads into text and image components using NLP and Computer Vision techniques, AI can help analyze the impact of individual creative elements on ad performance. For example, attention-based models could identify parts of the ad copy that most effectively engage users. The results can be mapped onto standard marketing frameworks like AIDA (Attention, Interest, Desire, Action) or RFM (Recency, Frequency, Monetary) to provide deeper insights into ad performance. Moreover, these insights can be presented in a more digestible format by generating text-based summaries using LLMs, easing the interpretation of complex data.

Considerations for Generative AI Implementation

• Cost - Utilizing pre-existing platforms such as Google’s AutoML, AWS Bedrock or ChatGPT can significantly inflate costs, especially considering that ChatGPT 4 is nearly ten times costlier than ChatGPT 3, necessitating careful deliberation on the appropriate use of GPT3 vs. GPT4. Training and hosting your own Machine Learning (ML) models may provide substantial savings at scale, albeit with an increased demand for resources and expertise for system maintenance. The recommendation is to explore efficiency strategies for these hosted solutions. One example includes a proof of concept, which involved labeling various parts of creatives at a cost of $1500 for 500 images, with additional training costs as a one-time $100 fee. Querying costs present two differing impacts on user experience: Option 1 ($1500 per month), which provides a dedicated, always-online instance for immediate compliance checks, and Option 2 ($150 per month), which necessitates batching requests in a CSV format and submitting them, yielding results typically within 1-2 hours. Long story short, the cost can vary widely and you need to explore what is the right option for you.

• Time - Although pre-existing solutions can expedite your AI implementation, training and hosting your own ML model can be a time-consuming process. Developing proficiency in maintaining your own solution also requires a significant investment of time.

• Expertise - While having an internal team proficient in AI/ML is advantageous, the rapid advancements in this field have made AI accessible to a large number of engineers. Hence, there should be no hesitation in exploring this area.

Challenges and Future Scope

The challenges in implementing generative AI lie in training models to generate brand-compliant design ideas and adapting ads from one size to another without losing their essence.

As we venture further into the world of generative AI, we anticipate a shift. Once the initial euphoria around generative AI subsides, use cases that serve the invariables - efficiency, productivity, and creativity - will persist, and the rest will fade away.

Embrace generative AI in your business. It is not just an option, but a necessary leap into the future. Happy coding, and until next time, may the AI be with you!

For more insights into our culture and processes, visit culture.rocketium.com. We foster a collaborative and open environment, working alongside talented and driven individuals. If you are eager to join our journey and be a part of this exciting adventure, please reach out to us at careers@rocketium.com.