Navigating the Generative AI Project Lifecycle

As briefly mentioned in Chapter 3, Exploring ML Algorithms, generative AI represents a category of AI focused on generating new data, such as text, images, videos, music, or other content, based on input data. This technology has the potential to transform numerous industries, offering capabilities previously unattainable. From entertainment to healthcare to financial services, generative AI exhibits a wide range of practical applications capable of solving intricate problems and creating innovative solutions.

In this chapter, we will embark on a practical journey, guiding you through the process of turning a generative AI project from a business concept to deployment. We will delve into the various stages of a generative AI project’s lifecycle, exploring different generative technologies, methodologies, and best practices. Specifically, we will cover the following key topics:

• The advancement and economic impact...

The advancement and economic impact of generative AI

Over the past decade, there has been remarkable progress in the field of generative AI, which involves the creation of realistic images, audio, video, and text. This advancement has been driven by increased computational power, access to vast internet datasets, and advancement in ML algorithms. Both open-source communities and commercial entities have played pivotal roles in pushing the boundaries of generative AI.

Prominent organizations like OpenAI, Stability AI, Meta, Google, the Technology Innovation Institute (TII), Hugging Face, and EleutherAI have contributed by open sourcing models such as GPT-2, OPT, LlaMA, Falcon, BLOOM, and GPT-J, fostering innovation within the community. On the commercial front, companies like OpenAI, Anthropics, Cohere, Amazon, and Google have made substantial investments in proprietary models like GPT-4, Claude, Cohere, Titan, and PaLM, leveraging cutting-edge transformer architectures and massive...

What industries are doing with generative AI

Enterprises across diverse sectors are actively engaging in the exploration of potential applications for generative AI technology, even though it is still early days in the adoption of generative AI. These enterprises are looking into this innovative technology to drive tangible business outcomes including increased productivity, enhanced customer experiences, novel business insights, and the creation of new products and services. With all the excitement surrounding this technology, it is also important to understand what’s practical and what is aspirational. With that in mind, let’s delve into some active areas of exploration of the adoption of generative AI.

Financial services

As leaders in technology adoption, financial services firms are actively exploring generative AI use cases across banking, capital markets, insurance, and financial data.

The majority of current generative AI applications focus on document...

The lifecycle of a generative AI project and the core technologies

The lifecycle for developing and deploying generative AI solutions spans multiple stages, with some variations from traditional ML projects, such as model customization and model evaluation. While certain phases like use case definition and data preparation align closely, stages including model development, training, evaluation, and adaptation take on unique characteristics for generative models.

Figure 15.1: Generative AI project lifecycle

At a high level, a generative AI project consists of a series of stages, including identification of business use cases, model selection or pre-training, domain adaptation and model customization, post-customization model evaluation, and model deployment. It’s important to recognize that while a generative AI project places significant emphasis on the capabilities and quality of the model itself, the model constitutes just one facet within the broader development...

The limitations, risks, and challenges of adopting generative AI

As powerful as generative AI technology is, it comes with its own set of limitations and challenges across multiple dimensions. In this section, we will delve into some of these concerns.

As most generative AI technologies such as LLMs generate responses based on conditioned probabilities, the outputs can be factually inaccurate or self-contradictory. They can even generate factually inaccurate responses with fluency and convincing tones, leading to difficulty in detecting misinformation by humans. This can create a multitude of problems, including erroneous decision making and negative social influence from misinformation. Moreover, it’s challenging to determine the source documents for the responses generated by generative AI models, which leads to difficulties in verifying facts and providing proper attribution.

Despite their impressive performance on standardized tests like BAR or SAT, LLMs have limitations...

Summary

In this chapter, we provided a comprehensive overview of the generative AI project lifecycle, from identifying business use cases to model deployment. We explored major generative technologies like FMs and key techniques for customization including domain adaptation, instruction tuning, reinforcement learning with human feedback, and prompt engineering.

The chapter also covered specialized engineering considerations around large model hosting and mitigating risks like factual inaccuracies. While limitations exist, responsible development and governance can allow enterprises across industries to harness generative AI’s immense potential for creating business value. With an understanding of the end-to-end lifecycle, practitioners can thoughtfully architect and deliver innovative yet practical generative AI solutions.

In the next chapter, we will talk about the key considerations for building a generative AI platform, retrieval-augmented generation (RAG) solutions...