??The Data Leader’s Edge: LLMs-Breaking It Down to the Data Level ??
Gary Cronin
Fractional Chief Data & AI Officer (EU Ireland) | Data Architecture, Data Governance, Data Engineering, Analytics & AI | TCS DATOM Certified
Welcome back to The Practical Architect. This week, we’re diving into Large Language Models (LLMs)—minus the jargon and holiday hype. Let’s unwrap what really matters: the data level.
?? Why Focus on Data? ??
To truly unlock AI’s potential, you need to understand how data flows through the system. Not just the shiny outputs, but the full lifecycle—from ingestion to prediction. Here’s why it matters:
?? Spot Issues Early:
?? Biases, hallucinations, and bottlenecks—easier to identify when you track the data layer-by-layer.
?? Strategic Alignment:
?? Understand how data interacts with the model to align AI initiatives with business goals.
?? Demystify AI:
?? Build confidence for your team, especially when tackling compliance and readiness challenges.
But here’s the festive surprise: the software developer in me was floored by the sheer amount of unit testing required for LLMs. It’s like Santa checking his list—not once, but twice for every layer and prediction.
As a Data Architect, though, I loved the simplicity: tagging and tracking data is just basic architecture patterns. ?? No need for a sleigh full of magic—just structured discipline applied where it counts.
?? Static Weights in BaU: The Gift of Consistency
Once an LLM is trained and deployed into a production environment for Business as Usual (BaU):
?? The weights remain static. These learned parameters dictate how the model processes input data and makes predictions.
?? Input prompts or queries don’t modify the model’s weights. The existing weights ensure consistent and predictable outputs.
This is where tagging and unit testing sparkle like tinsel on the AI tree:
?? Tagging and Tracking:
By applying basic data architecture principles, you can tag and track input data, ensuring its integrity and relevance for the model.
?? Validation Against Static Weights:
Rigorous testing ensures the model behaves consistently in predictable scenarios, which is key for trust in production.
?? Do We Need to Understand Every Calculation?
It makes sense that some aspects of the LLM data lifecycle might not be easily visualized in real-time. Similar to how we use functions like AVG or MAX in Relational Database Management Systems (RDMS) [the app or software you interact with when accessing or modifying items in a database] without diving into the underlying calculations, we might not always need to understand the minute calculations within an LLM.
Here’s why the analogy holds:
Abstraction for Usability:
RDMS functions abstract complex calculations to simplify data manipulation for users. Similarly, LLMs abstract intricate processes, allowing users to focus on the input (prompt) and output (response) without needing to grasp the internal workings.
Focus on Outcomes:
In both cases, the emphasis is on the results achieved rather than the precise steps involved. We trust the RDMS to calculate the average correctly, just as we rely on the LLM to generate coherent text based on its training.
?? Key Differences to Consider
Transparency and Explainability:
RDMS calculations are well-defined and transparent, while the decision-making processes in LLMs can be more opaque. This raises concerns about bias, fairness, and unpredictable outputs, which may necessitate deeper understanding in certain contexts.
Evolution of Understanding:
As technology matures, the need to "deep dive" into an LLM’s minute calculations may evolve—especially in applications requiring precision, fairness, and explainability (e.g., healthcare or finance).
?? Time Will Tell
The need to understand LLM calculations in greater depth will likely depend on specific use cases and criticality. For now, abstraction simplifies adoption—but as LLMs grow in importance, we may need to revisit their inner workings more frequently.
?? A Game-Changing Video
I was ready to write a detailed guide on how LLMs work—until I found this gem:
?? Why It’s Worth Your Time
Shows how data is ingested, transformed, and used to predict outputs.
2. ?? No Jargon, Just Data:
Accessible and transparent, focused on the flow of information.
3. ?? Hands-On Validation:
While not explicitly mentioned, here’s my takeaway: testing and tracking data at every step is as critical as putting a bow on a perfectly wrapped present.
?? What’s Your Learning Style?
How do you prefer to tackle complex data topics?
?? ?? Visuals: Diagrams, animations, and videos?
?? ?? Deep Dives: Technical walkthroughs with detailed explanations?
?? ?? Hands-On: Real-world examples and validation techniques?
Drop a comment below! Your input helps me tailor The Practical Architect to your style—and keeps the festive spirit alive. ??
?? The Data Leader’s Blueprint
Practical, clear, and built for action—that’s the blueprint.https://garyfccronin.substack.com/??