A Structured Framework for Gen AI Product Development

In our article Gen AI Strategy: Without strategy, execution is aimless. Without execution, strategy is just a daydream, we talked about the importance of establishing a comprehensive AI strategy for guiding Gen AI exploration and experimentation, ensuring they are aligned with broader business objectives, and delivering value in an ethical manner.

In this article, we will focus on addressing the Start Small component of the Think Big Start Small mantra. Our goal here is to try to cut through the mire and complexity, and offer a clear framework and actionable steps for companies that want to experiment with Gen AI. Some of this may be obvious to you, some may not be. But I hope this framework provides a simple and comprehensive approach to move ahead with purpose.    

A Structured Approach

Gen AI (and AI/ML in general) gives businesses unprecedented opportunities to enhance customer experiences, drive innovation, and optimize operations. Exponential advancements in Gen AI have opened opportunities to automate tasks, generate personalized content, analyze vast amounts of data, and streamline decision-making processes.  How do you keep up with the rate of change, while avoiding analysis-paralysis  in actually developing Gen AI products that deliver customer and business value? 

The framework below provides a straightforward view of the process, and the necessary and sufficient requirements for experimenting with Gen AI, and productionalizing Gen AI applications.

1. Define Use Cases
   • Work backwards from your internal and external customers to identify valuable capabilities and challenges to address. 
   • Engage your entire organization to uncover valuable insights and ideas for enhancing customer experiences or streamlining processes. 
   • Prioritize use cases by business value  -  customer experience, revenue growth, operational efficiency, productivity, cost optimization. Other factors that will come into play later include data availability, implementation complexity, computational resources, and time to value realization 

2. Data Collection
   • Acquire the right type of nuanced and complex data needed for your use cases from sources such as  your internal data, public data sets, web scraping, crowdsourcing, and synthetic data generation. 
   • Ensure compliance with data privacy laws and guidelines on data collection, storage, and usage. Include ethical considerations into your data analyses, such as addressing potential biases in the training data and ensuring privacy

3. Data Processing
   • Cleanse your data to address  inconsistencies, missing values, outliers, and errors to ensure accurate training.  Apply normalization and standardization to ensure that one feature does not unduly influence the model. Perform data augmentation as needed (increasing dataset size, transformations like image rotations or synonym replacements).  
   • Perform feature extraction and normalization to create a more informative dataset for classification, prediction, or clustering, and help address scale sensitivity and performance. Perform data labeling - annotating or categorizing data to facilitate effective AI learning
   • Create embeddings to transform high-dimensional and categorical data into vector representations, capturing meaningful patterns, relationships and semantics that can be understood by machines
4. Model Selection
   • Choose the appropriate foundational models from numerous proprietary and open source options, considering factors such as task specificity, performance, accuracy, computational demand, scalability, and ethical considerations.
5. Model Training
   • Choose  training techniques to balance your speed, complexity, cost, and accuracy requirements.
   • Prompt Engineering allows crafting prompts and pipelines to guide GenAI behavior,, and uses “Zero shot” or “Few Shot” learning to align responses with expectations
   • Retrieval Augmented Generation (RAG) combines an LLM with custom enterprise data, enhancing response relevance and reduces risk of outdated or hallucinated information 
   • Parameter Efficient Fine Tuning (PEFT), or just Fine Tuning, adapts a pre-trained LLM to specific datasets. It updates a small number of the model’s parameters on task specific data
   • Pretraining involves train a Gen AI model from scratch, and requires a massive amount of high quality data and computational resources
6. Model Evaluation
   • Continuously evaluate and refine models through manual reviews, metric analysis, validation datasets, and iterative refinement processes.
   • Manual review of generated outputs can identify glaring errors, biases, or inconsistencies. Some Gen AI applications like Content or Copy generation will likely always be manually evaluated and updated before being used . Metrics such as BLEU, ROUGE, and METEOR scores can be used to compare generated text, and Frechet Inception Distance (FID) or Inception Scores can be used to quantify realism and diversity of generated images. Validation through test datasets the model has never seen before can be performed to measure generalization capability, ensure the model is not overfitting the training data, and assist with hyperparameter tuning
   • Iterative model refinement may be required to ensure ongoing  optimal performance based on evaluations, feedback, and monitoring drift. Model refinement approaches include hyperparameter tuning, architecture adjustments, further fine-tuning, regularization and dropout, feedback loops, and adversarial training.   
7. Deployment and Monitoring
   • Deploy models in production, ensuring scalability, monitoring performance metrics, gathering user feedback, and maintaining security and ethical standards.
   • Select your compute and inference infrastructure based on the size and complexity of your models. Cloud platforms like AWS SageMaker, GCP AI Platform, and Azure Machine Learning  facilitate scaling and managing deployed models. For easy access by applications or services, models are often deployed behind APIs using frameworks like FastAPI or Flask.
   • Monitor performance metrics like latency, throughput, and error rates. Setup mechanisms to gather user feedback, ensure traceability by maintaining detailed logs of all model predictions, follow security best practices for data encryption, authentication, and vulnerability management, and setup mechanisms to monitor harmful behaviors or unintended results.

the art of the possible

The field of Gen AI is buzzing with innovation, promising to revolutionize everything. The amount of information generated every day is staggering to say the least. And yet, it is imperative for executives and leaders of organizations to absorb as much as they can about the disruptive potential of Gen AI. Insights reports such as McKinsey’s Economic Potential of Gen AI and Sequoia Capital’s Generative AI’s Act Two can be informative.

If you are interested in learning more through executive workshops that provide invaluable insights, explore real-world use cases, showcase best practices for implementing Gen AI solutions that focus on business value, and guide you through the process of developing a comprehensive AI strategy, please reach out to us.