26/100 - Planning for GenAI Projects | Project Phases for Building GenAI LLMs

Updated: Feb 16

In artificial intelligence, the emergence of foundational models has revolutionized our approach to creating new AI models. These pre-trained behemoths, equipped with a vast understanding of language, concepts, and patterns, provide a robust starting point for developing specialized AI applications. This breakthrough simplifies the process of AI model creation, enabling faster development and deployment, and opening up new possibilities across various domains.

GenAI Project Scope

The journey to build and utilize these models begins with the foundational models themselves. Preparing data for these models is a meticulous process, involving careful data selection, processing, and filtering, eliminating duplicates etc to ensure quality and relevance thereby enhancing the model's ability to learn effectively.

Training the model requires understanding of tokens, which are the building blocks of language for AI. This stage involves teaching the model to recognize, interpret, and generate human-like text based on the input tokens. The validation phase follows, where the model's performance is rigorously tested against various parameters to ensure accuracy and reliability. Finally, deploying the model, where it is made available for real-world applications. This step involves integrating the model into user-facing applications, ensuring scalability, and maintaining performance consistency. The deployment phase translates the theoretical capabilities of the model into practical, usable tools and solutions.

This blog aims to provide a structured approach to planning and executing Generative AI projects, with a focus on creating tangible business impact and ensuring technical excellence.

In the previous article we looked at 'Mastering SAFe PI Planning'. In this article, we will explore how to plan for Gen AI projects by training LLMs in detail through below sections:

GenAI Project Scope
Alignment with Business Vision and Strategy
Building the GenAI LLM Models
Setting up a Cross functional GenAI Project Team
Recommended Readings

Alignment with Business Vision and Strategy

1. Setting Expectations with Business Stakeholders

Key Objective: Align project outcomes with business goals.
Approach: Conduct workshops to understand business needs and clearly define how the LLM will address these needs. Refer PSHQ blog on Stakeholder Identification & Engegement provides more insights on working with stakeholders.

2. Create a Roadmap and Start Sprinting

Key Objective: Implement Agile methodologies for project execution.
Approach: Break down the project into sprints, with each sprint delivering a potentially shippable product increment. Refer PSHQ blog Product Road Mapping on how to create a roapmap and its components for innovation projects.

3. Metrics for Comparing/Evaluating LLMs

Key Objective: Establish criteria for assessing LLM performance.
Approach: Use metrics like accuracy, response time, and user satisfaction for evaluation. Refer article KPIs for GenAI to deep dive.

Building the GenAI LLM Models

1. Choice of LLMs for Generating Responses

Key Objective: Select the most suitable LLM for the project.
Approach: Compare various LLMs like GPT, BERT, and their variants based on project needs.

Choosing the right LLM for generating responses is a decision that hinges on several factors, including the model's size, training data, and intended application. Each LLM, whether it's GPT-3, BERT, or others, has unique characteristics in terms of understanding context, generating creative responses, and handling specific language tasks. The choice depends on the specific requirements of the project, such as the need for creative content generation, data analysis, or customer interaction, balancing factors like computational resources, accuracy, and response generation speed. Refer this blog for a comparitive analysis of different GenAI models - Comparative Analysis of Large Language Models.

2. Understand the Quality of Data Used to Train the Models

Key Objective: Ensure high-quality training data for accurate models.
Approach: Perform data analysis and cleansing to improve model training outcomes.

The foundation of any effective LLM is the quality of the data used for training. Ensuring high-quality, diverse, and representative training data is paramount for the model's accuracy and ability to generalize across various scenarios. This involves rigorous data cleaning, preprocessing, and augmentation techniques to enhance the dataset's quality. Additionally, understanding the source and nature of the data helps in identifying any potential biases or limitations in the training dataset, which can significantly impact the model's performance and fairness. More details on significance of data quality, here in this blog What is the role of Data Quality in LLMOps?

3. Choosing the Right Model Tuning Method

Key Objective: Optimize the LLM for specific use cases.
Approach: Explore techniques like fine-tuning or transfer learning based on project requirements.

The foundation of any effective LLM is the quality of the data used for training. Ensuring high-quality, diverse, and representative training data is paramount for the model's accuracy and ability to generalize across various scenarios. This involves rigorous data cleaning, preprocessing, and augmentation techniques to enhance the dataset's quality. Additionally, understanding the source and nature of the data helps in identifying any potential biases or limitations in the training dataset, which can significantly impact the model's performance and fairness. Additional info on model tuning - What is model tuning?

4. Prompt Engineering

Key Objective: Enhance model output through effective prompts.
Approach: Develop structured and clear prompts to guide the model's responses

Prompt engineering is the practice of strategically designing and refining the input (or "prompt") given to an AI model, especially a language model, to elicit the most accurate and relevant response. It involves crafting prompts that are clear, contextually appropriate, and aligned with the model's training. For example, instead of asking a language model, "What's the weather?", a more effective prompt would be, "What is the current weather forecast for New York City on December 16, 2023?". This refined prompt provides specific context, leading to a more precise and useful response.

5. Choosing the Right Embedding Technique & Retrieval Mechanism

Key Objective: Improve the relevance and contextuality of model responses.
Approach: Evaluate embedding techniques like BERT or GPT for contextual understanding.

Embedding techniques in AI involve converting text or other data into numerical vectors, which represent the underlying meaning or features in a format that machines can process. For example, words with similar meanings are represented by vectors that are close in the vector space, enabling the AI to understand semantic relationships. Retrieval mechanisms, on the other hand, are methods used by AI systems to fetch relevant information or responses based on these embeddings. For instance, in a chatbot, when a user asks a question, the retrieval mechanism uses embeddings to find the most relevant answer from a database of responses.

6. Creating a Consumption Layer

Key Objective: Develop an interface for users to interact with the LLM.
Approach: Design a user-friendly API or GUI that makes the LLM accessible to end-users.

Next step in developing our Generative AI solution is establishing a robust consumption layer, which serves as the interface for users or systems to interact with the AI model. This typically involves developing an API (Application Programming Interface) that enables seamless and secure communication between the AI model and its end users. This layer must be scalable, to handle varying load and usage patterns, and designed with a focus on user experience, ensuring ease of use. It should also integrate effectively with existing systems and platforms, providing a consistent and efficient way for users to leverage the AI capabilities.

7. Test, Demo, and Launch to Production

Key Objective: Ensure the model is production-ready.
Approach: Conduct rigorous testing, gather feedback through demos, and make necessary adjustments before launching.

In a GenAI project, the "Test, Demo, and Launch to Production" phase encompasses finalizing the model for real-world application. Testing involves rigorously evaluating the model's performance, such as its accuracy in understanding and generating language, using diverse datasets. For example, testing might include assessing the model's response quality across various topics. Demos provide stakeholders with a practical showcase of the model's capabilities, like demonstrating a customer service chatbot's ability to handle inquiries. Finally, launching to production means deploying the model into a live environment, where it might be integrated into a website or app to interact with users, complete with monitoring and maintenance plans to ensure ongoing effectiveness and reliability.

Setting up a Cross functional GenAI Project Team

Assembling a cross-functional Scrum team with a mix of roles not only fosters agility and collaboration but also ensures that the project benefits from a broad spectrum of expertise. Here, we outline an optimized team structure, delineating roles into dedicated and shared resources to balance core competencies with typical resource availabilities.

Dedicated Resources

These roles are pivotal to the project, focusing on essential tasks that drive the development and deployment of the GenAI application.

Product Owner (PO)

Responsibilities: Leads in defining the project vision, managing the product backlog, and ensuring alignment with user and business needs.
Deliverables: Product vision statement, prioritized backlog, detailed user stories, and acceptance criteria.

AI/ML Engineers (including Data Engineer)

Responsibilities: Responsible for designing, developing, and training AI models. A Data Engineer within this group focuses on optimizing data workflows and infrastructure to support model training.
Deliverables: AI model designs, trained AI models, data pipeline architecture, and model evaluation reports.

Architect

Responsibilities: Oversees the application's overall architecture, ensuring it is scalable, performant, and capable of integrating with existing systems.
Deliverables: System architecture documents, technical specifications, and scalability strategy.

Quality Assurance (QA) Engineer

Responsibilities: Implements comprehensive testing strategies to ensure the application meets quality standards.
Deliverables: Test plans, automated test scripts, bug reports, and quality assurance reports.

Frontend Engineer

Responsibilities: Develops the user interface, focusing on creating a responsive, accessible, and engaging user experience.
Deliverables: UI codebase, performance optimization reports, and compatibility test results.

Shared Resources

These specialized roles support various aspects of the project, providing their expertise across multiple teams or projects as needed.

Scrum Master / Program Manager

Responsibilities: Ensures the Scrum process is followed, facilitates communication, and addresses impediments, possibly managing multiple projects.
Deliverables: Sprint planning and review documentation, communication plans, and an impediment resolution log.

DevOps Engineer

Responsibilities: Manages continuous integration/continuous deployment (CI/CD) pipelines, oversees application deployments, and ensures the infrastructure is scalable.
Deliverables: CI/CD configurations, deployment scripts, and infrastructure automation scripts.

UX Designer

Responsibilities: Designs the application's user experience, ensuring it is intuitive and meets the needs of the end-users.
Deliverables: UI/UX designs, usability testing reports, and design system guidelines.

Conclusion

Planning and executing a Generative AI project requires a balanced approach that encompasses business alignment, technical acumen and program excellence. By methodically addressing each aspect of the project, from stakeholder engagement to model deployment, Project/Program Managers can steer these groundbreaking projects to success, creating significant business impact and driving innovation.