How to Build Generative AI Solutions from Scratch

Step 1: Identify the Problem

Why do you want to build a generative AI solution?

Every software development project works toward some end goal, whether it is generating human-like computer voices, summarizing long paragraphs of research papers, or an AI tool that generates new images from a few simple prompts.

This first step is crucial, as it lays down the landscape for every following step. The more detailed this step, the easier and faster it is to proceed.

Step 2: Data Collection

Building a generative AI naturally comes with thorough data collection steps. The AI model needs to be fed massive amounts of data to produce high-quality responses that meet the customer’s expectations. Key points of data collection include:

• Identifying the right data sources to ensure data quality and authenticity. The data can come from sensor outputs, web scraping, etc. Double-check to make sure the data is relevant and accurate in the matter the model is trained in.
• The collected data should be large in volume and vast in diversity. The more diverse the data you feed to the AI model, the more diverse and better the answers overall.
• Generative AI is only as good as the data you use to train it. Hence, make sure to process and clean the data, e.g., remove duplicates or handle missing values, to ensure the data used is of the highest quality.
• Check for ethical and legal compliance of your data.
• Implement data management tools to keep track of the changing data as the generative AI solution evolves. This ensures a structured AI development.

Step 3: Data Processing and Labeling

After being collected, the data is prepped for AI training. This includes cleaning, normalizing, and augmenting the data.

• Data cleaning removes any errors and inconsistencies.
• Data normalizing ensures a uniform data scale.
• Data augmentation diversifies the dataset.

The data then extracts key characteristics from the raw data, improving model performance. It also goes through training, validation, and test sets and is manually or semi-automatically categorized or labeled.

Step 4: Model Selection

After thorough preparation, it is time to choose the AI’s foundational model and appropriate algorithms. Instead of conducting data collection and meticulous preparation, most start-ups use pre-trained models as a starting point to gain access to a quality dataset, accurate model performance, and shorten the time it takes to train an AI model. Some examples of such foundational models are GPT, Amazon Titan, Claude, and BERT. From here, developers can fine-tune the model, significantly shortening the development time.

To choose the right model, teams shouldn’t focus solely on its primary capabilities but also on its additional features and response qualities.

Step 5: Selecting Tech Stack

Best Practices

AI models need to handle large amounts of data. Hence, its architecture needs to be robust while performing and scaling well.

To achieve this, some of the best practices include:

• Breaking down the AI solution into small, manageable components. In other words, microservices architectures are a great choice for developing generative AI solutions.
• Implement load balancing to achieve peak performance and prevent crashing.
• Implement message queues to establish effective communications between the solution’s components.
• Use caching to reduce backend requests.
• Customize the model’s metrics like batch size, dropout rate, etc.

Tech Stack

Here is a quick overlook of a generative AI solution tech stack that is scalable, reliable, and ethical.

• Foundation models & pre-trained models: GPT for text generation, DALL-E and Stable Diffusion for image generation, and Codex for code generation.
• Deep learning libraries and frameworks: TensorFlow, PyTorch, and JAX
• Data handling and processing: Pandas, NumPy, Matplotlib
• Model optimization and acceleration: ONNX, TensorRT
• Model deployment and serving: TensorFlow Serving or TorchServe, paired with containerization (e.g., Docker) and orchestration (e.g., Kubernetes)
• MLOps & model lifecycle management: Kubeflow or MLflow
• Edge deployment and mobile integration: TensorFlow Lite or Core ML
• Inference serving and scaling: NVIDIA Triton Inference Server and KFServing
• Ethics, bias, and fairness tools: Fairlearn or IBM AI Fairness 360

Step 6: Evaluation and Refinement

After training, you need to gauge and refine the model’s performance. This is a constant process. This step can be done by an in-house team or you can outsource QA experts.

Whatever method you choose, here are a few key ideas to keep in mind:

When evaluating the model, use BLEU, ROUGE, or METEOR metrics for text generation. Track loss functions to gauge prediction accuracy. Conduct manual inspections to uncover any errors or biases that quantitative metrics might miss.

Model refinement involves optimizing parameters, adjusting the solution’s structure, and using techniques like transfer learning to improve performance. Continuous improvements are made by gathering user feedback, monitoring for data changes, and testing robustness through simulated challenges.

Step 7: Deployment and Integration

After rigorous preparation and testing, it’s time to set up cloud computing systems or on-premise infrastructure for the right deployment environment, depending on the requirements. Carefully set up hardware and software for the operating systems, databases, and servers. Don’t forget the frameworks and libraries essential for running the AI solution. Lastly, set up continuous integration and deployment (CI/CD) pipelines with tools like Gitlab.

Step 8: Monitoring and Maintenance

All the hard work doesn’t end at the deployment step. Monitoring the solution’s performance is just as important. Keep a close eye on the metrics and gather feedback from users. From there, refine the algorithms, fix bugs, and update the solution with the latest technologies.