GenAI Demystified
When I started the GenAI Demystified series in April 2024, I had a simple goal: cut through the noise surrounding generative AI and provide practical, actionable guidance for professionals working with Oracle technologies. Nearly two years and 18 articles later, this collection has become something more—a comprehensive roadmap for organizations and practitioners seeking to harness generative AI without abandoning their investment in proven data platforms.
Why I Created This Collection
Working at the intersection of enterprise data management and artificial intelligence, I've witnessed a persistent disconnect. AI practitioners often lack deep database expertise, while database professionals struggle to navigate the rapidly evolving AI landscape. Meanwhile, organizations are bombarded with GenAI hype that promises transformation but delivers little practical guidance on implementation.
I created GenAI Demystified to bridge this gap—to provide the technical depth, strategic frameworks, and honest assessments that enterprises actually need to succeed with generative AI.
A Holistic Approach: Technical, Conceptual, and Strategic
The collection addresses three critical dimensions that I believe are essential for any successful GenAI implementation (Technical Implementation, Conceptual Foundations, and Strategic Application):
Technical Implementation: Making It Work
Let me share some of the deeper technical explorations I've undertaken:
Natural Language to SQL with Oracle 23ai's SelectAI has become one of my most referenced pieces, and for good reason. While the promise of converting natural language to SQL sounds straightforward, the reality is far more nuanced. In this article, I dive into optimization techniques that can mean the difference between a chatbot that frustrates users and one that delights them. I explore prompt engineering strategies specific to SelectAI, discuss how to handle ambiguous queries, and provide concrete examples of tuning the AI's understanding of your specific database schema. The key insight? Success with SelectAI isn't just about the technology—it's about understanding how your users think about their data and bridging that mental model with your database structure.
Oracle Database 23ai Vector Search marked a pivotal moment in my journey with this technology. When Oracle introduced vector search capabilities, I knew this wasn't just another feature—it was a fundamental shift in how we think about database queries. In this comprehensive article, I break down the architecture of vector search, explain the mathematics behind semantic similarity (without requiring a PhD to understand it), and most importantly, provide real-world use cases where vector search outperforms traditional approaches. I discuss embedding models, distance metrics, and indexing strategies, but always with an eye toward practical application. One of my favorite talking points on vector search explores a retail scenario where vector search enables semantic search (say, "find similar products") functionality that would be nearly impossible with traditional SQL.
Essential Design Skills for Oracle 23ai Vector Search takes the foundation from the earlier vector search article and elevates it to a design discipline. Here, I tackle questions that architects and senior developers face: How do you choose the right granularity for your embeddings? What are the trade-offs between accuracy and performance? How do you design your schema to support both traditional and vector queries efficiently? I share lessons learned from actual implementations, including detailed design alternatives capable of handling semantic search on enterprise data in a relational database.
The article on OCI RDMA Superclusters represents my deep dive into infrastructure—a topic that often gets overlooked in AI discussions. RDMA (Remote Direct Memory Access) technology isn't glamorous, but it's crucial for high-performance AI workloads. I explain how OCI's RDMA implementation provides the low-latency, high-bandwidth connectivity that large language models need for efficient training and inference. This article became particularly relevant as organizations moved beyond experimentation to production-scale deployments where infrastructure choices have significant cost and performance implications.
Conceptual Foundations: Understanding What We're Actually Building
Technical skills alone aren't enough. We need to understand the fundamental nature of these systems:
The Heisenberg Uncertainty Principle and Bias in Generative AI emerged from late-night reflections on a persistent problem: the more we try to eliminate bias from AI systems, the more we seem to introduce new biases through our correction mechanisms. Drawing a parallel to quantum physics might seem unusual, but the Uncertainty Principle offers a powerful metaphor. Just as we cannot simultaneously know both position and momentum with perfect precision, we cannot simultaneously optimize for complete neutrality and useful specificity in AI systems. Every intervention we make affects the system in ways that create new uncertainties. This article challenges the notion that we can build "perfectly unbiased" AI and instead advocates for transparency about the trade-offs we're making.
On the Limitations of GenAI Large Language Models is perhaps my most sobering piece—and intentionally so. As someone deeply invested in this technology, I felt a responsibility to be honest about what LLMs cannot do. I explore the lack of true reasoning, the hallucination problem, the context window constraints, and the fundamental limitation that these models are pattern matchers, not knowledge systems. I discuss why LLMs struggle with mathematics, why they can't reliably fact-check themselves, and why treating them as databases is a category error. This isn't pessimism—it's practical wisdom. Understanding limitations prevents costly mistakes and helps us apply the technology where it actually excels.
Semantic Understanding and Reasoning in GenAI Models takes a different angle, exploring what these models can do and why it seems almost magical. I break down the difference between statistical pattern matching and genuine semantic understanding, examining whether current models truly "understand" language or are simply very sophisticated prediction engines. This philosophical-technical exploration helps readers develop intuition about when to trust model outputs and when to be skeptical.
Strategic Application: Connecting Technology to Business Value
Technology without strategy is just expensive experimentation:
Prioritizing GenAI Use Cases – A Structured Approach has become my most shared article among business leaders. I developed a framework that evaluates potential GenAI use cases across multiple dimensions: technical feasibility, business impact, data readiness, and organizational capability. Too many organizations approach GenAI with either unbridled enthusiasm (let's AI everything!) or paralyzing caution (it's too risky). My framework provides a middle path—a systematic way to identify high-value, achievable use cases while building organizational competency. I include a scoring methodology, real examples from multiple industries, and honest discussions about when to say "not yet" to certain use cases.
Oracle GenAI Stack: RAG vs. Fine-Tuning for Real-World Workloads addresses one of the most common questions I receive: should we use Retrieval-Augmented Generation or fine-tune a model? The answer, as I explain in detail, isn't binary. I compare both approaches across multiple criteria: cost, maintenance overhead, data requirements, update frequency, and accuracy for different types of queries. I provide decision trees, cost models, and implementation examples for both approaches. One key insight that resonates with readers: RAG often wins for enterprise applications because it separates knowledge from the model, making updates straightforward and keeping costs manageable.
Accelerate Your Generative AI with Oracle: A Business Process Perspective represents my most recent thinking on AI integration. Rather than treating GenAI as a standalone technology, I explore how it weaves into existing business processes. I encourage teams to focus on understanding the business processes, identifying high-value use cases, rather than wrestling with infrastructure complexities. This article was designed to provide a mental framework that bridges the gap between IT and business leadership.
Practical Resources for Every Practitioner
Throughout the series, I've been committed to making this knowledge accessible:
My study guide for the OCI Generative AI Professional Certification emerged from my own certification journey. Rather than just listing topics, I share my actual study strategy, including the power of creating a taxonomy of terms—a technique that helped me organize the vast landscape of GenAI concepts. I discuss which areas deserve deep study versus surface-level familiarity, and I share resources beyond the official documentation.
The workshops for GenAI application development article provides curriculum frameworks for teams looking to upskill. I outline hands-on exercises, prerequisite knowledge, and learning pathways for different roles—from DBAs exploring AI to developers building their first RAG application.
Privacy, Security, and the Enterprise Reality
How Does OCI Generative AI Services Provide Privacy and Security? addresses concerns that keep CISOs awake at night. I detail how OCI’s Generative AI Service offerers not only cutting-edge AI capabilities but also robust security and privacy features. With dedicated GPU clusters, data isolation, and leveraging OCI security services, OCI ensures that customer workloads are protected at all times. These security measures underscore OCI's commitment to providing a secure and reliable platform for AI innovation.
The Evolution Continues
The most recent articles on SelectAI optimization and business process perspectives reflect how my thinking has evolved. Early in the series, I focused on foundational concepts—what is vector search, how do LLMs work? As Oracle's AI capabilities matured and as my own experience deepened, the articles have shifted toward optimization, integration, and strategic application.
This evolution mirrors the broader industry shift from "what is GenAI?" to "how do we make it work for our specific needs?" I'm now exploring topics like cost optimization, monitoring and observability for AI systems, and advanced RAG architectures—all grounded in the Oracle ecosystem.
Who This Collection Serves
I wrote GenAI Demystified for several audiences, often simultaneously:
- Database administrators discovering that their expertise in data management is more relevant than ever—vector indexes aren't that different from B-tree indexes once you understand the principles
- Developers navigating the gap between proof-of-concept demos and production-ready applications
- Architects making infrastructure and design decisions with long-term implications
- Business leaders evaluating which AI investments will deliver actual ROI versus which are just following trends
- Certification seekers and continuous learners who want depth, not just breadth
My Commitment Going Forward
GenAI Demystified isn't finished. As Oracle continues to innovate and as the broader AI landscape evolves, I'm committed to documenting the journey—the successes, the failures, and the lessons learned. Upcoming topics I'm exploring include multi-modal AI with Oracle, advanced prompt engineering patterns, and the emerging discipline of AI observability.
An Invitation
This collection represents nearly two years of research, experimentation, implementation, and reflection. It's born from real projects, real challenges, and real conversations with professionals navigating the same journey. My hope is that it serves as more than just a technical resource—that it becomes a companion for your own exploration of what's possible when enterprise data meets generative AI.
Whether you're just beginning your GenAI journey or you're deep into production deployments, I believe there's value in these pages. The technology will continue to evolve, but the principles—understand deeply, apply strategically, remain honest about limitations—will endure.
Explore the complete GenAI Demystified collection at: [www.rogercornejo.com/genai-demystified](https://www.rogercornejo.com/genai-demystified)
I invite you to join me on this journey. Read, experiment, question, and share your own discoveries. The future of enterprise AI is being written right now, and I'm honored to contribute to that story.