Unpacking Generative AI — Beyond 100 Lines of Code
After dedicating more than a year to hands-on work with Generative AI (GenAI) across the realms of data management and banking, with over 20 diverse use cases under my belt, a curious observation emerged: most of these use cases boil down to no more than 100 lines of code. This might suggest that leveraging GenAI is a straightforward endeavor. However, its not that easy.
The simplicity of the code masks the complex underpinnings that constitute the foundation of any successful GenAI implementation. Here’s what lies beneath:
🔍 Understanding the Use Case: The Forefront of Innovation
The first, and arguably most crucial, step involves a deep dive into the specific use case, determining how GenAI can add value, whether through process acceleration, automation, or generating a new outcomes. This stage is less about coding and more about envisioning how GenAI can revolutionize conventional tasks or processes through innovation.
🤖 The Power of Synergy: Combining GenAI with Other Technologies
While GenAI is powerful on its own, its true potential is unleashed when synergized with other technologies. Knowledge Graphs and Vector Stores exemplify this, enhancing GenAI’s capacity to handle complex scenarios. This integration significantly adds layers of complexity beyond straightforward code implementation.
⚙️ The Process Itself: Engineering, Refinement, and Tuning
The deployment process of GenAI solutions involves nuanced steps: prompt engineering, Retriever-Augmented Generation (RAG), and model fine-tuning. Each phase demands meticulous attention, experimentation, and refinement to align the AI’s output with the project goals, showcasing the iterative nature of this technology.
🌱 The Emergence of Small Language Models: A Glimpse into the Future
An emerging trend in the GenAI landscape is the development of small language models. These models promise to accelerate the adoption of GenAI technologies by offering more efficient training processes and addressing data privacy concerns, a prevalent issue with their larger counterparts. While this topic warrants a discussion of its own, it signifies a shift towards more accessible, privacy-conscious AI applications.
Conclusion:
The path to implementing GenAI in real-world scenarios extends well beyond mere 100 lines of code. It encompasses a profound understanding of the problem domain, strategic tech integration, detailed process engineering, and keeping pace with emerging trends like small language models. The essence of our work in GenAI lies not in the code itself but in the creative application of technology to tackle complex issues and generate value. Stay tuned for my upcoming post, where I’ll explore the impact of small language models on the future of GenAI more deeply.
