GenAI & Custom LLM Solution Architectures: A Roadmap for Data Professionals

• How can we maximize data utility?
• What tools can make AI implementations efficient and scalable?

1: DATA SOURCES 
The Foundation

The architecture begins with diverse, high-quality data sources. Effective AI models require access to both internal and external data streams:

• Internal Sources: This includes structured data from data warehouses, unstructured documents, and any proprietary datasets that contribute unique insights.
• External Sources: Often leveraged from IoT, social media, and web data, these sources provide an expanded context and variability to enrich the AI's knowledge.

Utilizing these foundational sources helps set the stage for robust model training and operational stability.

2: DATA ENGINEERING & SYNTHETIC DATA  Enhancing Data Quality

Once data is gathered, it’s essential to prepare it for GenAI workflows. This involves:

• Data Engineering: Extracting, cleansing, and anonymizing data to ensure ethical and effective AI use. Masking sensitive information not only safeguards privacy but also enhances compliance.
• Augmented and Synthetic Data: To boost data variability and volume, leveraging semi-synthetic data (generative AI-enhanced), or synthetic data (generative AI-created), can fill gaps where real-world data is scarce or unavailable. This synthetic data is invaluable for creating realistic test cases and exploring scenarios otherwise difficult to simulate.

Together, these practices enable a solid data foundation, fostering reliable outcomes in later stages.

3: PROMPT ENGINEERING
Guiding the Model's Performance

Prompt engineering is an essential skill for maximizing model effectiveness in GenAI. Techniques in this layer are divided into simple and complex approaches:

• Simple Prompts: Basic I/O prompting, chain-of-thought, and one-shot prompting are commonly used to initiate model responses and clarify objectives.
• Complex Prompts: Advanced strategies like recursive prompting, tree-of-thought, and hybrid model prompting (e.g., reflection models) allow for nuanced responses that adapt over time. For example, a recursive prompt can refine outputs iteratively, mimicking a deeper understanding.

By carefully designing prompts, professionals can guide AI models to perform tasks with greater accuracy and relevance, transforming raw data into valuable insights.

4: LARGE LANGUAGE MODELS (LLMs) 
Choosing the Right Engine

In this layer, selecting an LLM tailored to the specific business use case is critical. Options include:

• API-Based Models: These include OpenAI's GPT-4, Google’s Gemini, and Microsoft’s Copilot, ideal for organizations preferring managed solutions.
• Self-Hosted Models: Organizations with unique privacy or security needs might opt for self-hosted models like Meta’s LLaMA 2 or Google’s BERT, which allow for in-house control over data and model tuning.

This flexibility enables data professionals to choose the most suitable model based on organizational goals, whether for innovation, data sensitivity, or resource allocation.

5: APPLICATIONS
Transforming Insights into Actionable Solutions

With the right data and model, the final layer involves deploying AI applications to meet organizational needs:

• Operational Applications: Examples include generating synthetic test data, forecasting, and fraud detection—use cases that benefit from GenAI’s ability to replicate real-world complexities.
• Interactive Applications: For interactive, user-focused applications, solutions like hypothetical analysis, solution advising, and A/B testing provide real-time, adaptive insights that engage end-users effectively.

This application layer is where GenAI models become practical tools, helping businesses automate tasks, make data-driven predictions, and ultimately transform operations.

FINAL THOUGHTS:
The Practical Roadmap for Data Professionals

With a layer-based approach, data professionals gain a structured path to implementing LLMs. This roadmap transforms GenAI from a complex idea into a tangible toolset, empowering organizations to harness data more strategically. From data engineering to advanced prompt design and model selection, each layer addresses a key part of the GenAI journey, making the deployment of custom LLMs more practical and impactful than ever.

Whether building scalable applications or enhancing operational workflows, the future is bright for data professionals ready to tap into the full potential of GenAI.

How can GenAI amplify your organization's current capabilities?
Be sure to check out Online's GenAI Workshops for identifying the best
use cases and assessing your data readiness. 

About the Author

Steven Holt is a visionary Data and GenAI expert with over 14 years of industry experience, specializing in Enterprise Data Warehouse, Business Intelligence, and Decision Intelligence solutions. As a Data Strategy and Digital Innovation Leader, he has a proven track record of delivering transformative solutions that align technology with business objectives, enhancing both customer and employee experiences through data-driven decision-making and agile methodologies.

In his role as a Data Competency Lead and as a member of the Innovation Lab at Online Business Systems, Steven combines his deep expertise in data strategy, analytics, and governance with cutting-edge GenAI. His strategic vision and collaborative approach empower businesses to integrate AI with enterprise data solutions, enhancing decision-making processes and driving innovation. Committed to pushing the boundaries of technology, Steven excels at fostering growth and efficiency through innovative, data-centric strategies.