When talking with colleagues who work on service development, I often notice a vague sense of burden regarding AI adoption. Digging into the root of that burden, it usually stems from misunderstandings caused by the term ‘Training’.
“If I attach a model to my service, will it consume user data and learn in real-time to get smarter?” “Can we control that learning process? What if it learns something weird?”
If you’ve had these concerns, you can rest easy for a moment. Today, I’ll clarify this misunderstanding in developer terms.
‘Training’ is Build Time, ‘Inference’ is Runtime
The first thing to correct is that the AI models we deploy to services are mostly in a ‘Frozen’ state.
In development terms, it works like this:
| Concept | AI Term | Development Analogy | Description |
|---|---|---|---|
| Creating a Model | Training | Build Time | Requires enormous resources and time |
| Using a Model | Inference | Runtime | Receives requests and returns results |
When we operate a service, the running binary code doesn’t modify itself and evolve. AI is the same way.
Once a model is deployed, it’s no longer a ’learning student’ but a ‘worker following instructions’. What we need to worry about isn’t training, but creating an environment where this worker can do their job well.
Understanding AI Architecture Through a Library Analogy
So how do we reflect the latest information, and what role does AI play? I often compare this structure to a ’library’.
| Component | Library Analogy | Description |
|---|---|---|
| AI Model (LLM) | Librarian | An entity with language and reasoning abilities |
| RAG / DB | Book Stacks | Space where service data and latest information is stored |
| Prompt | Work Instructions | Guidelines given to the librarian when assigning tasks |
What we service developers do daily is ‘adding new books to the library (DB updates)’. The librarian (AI) simply retrieves the books we’ve added when needed, reads and combines the content, and delivers it to users (Inference).
The ‘Learning (Fine-tuning)’ that many fear corresponds to ‘sending the librarian to graduate school for re-education’. This is only necessary as an edge case when the librarian lacks basic literacy skills or needs to handle specialized domains that general knowledge can’t address.
Most business problems are solved not by re-educating (training) the librarian, but by these three approaches:
- Hire a smart librarian - Choose a good base model
- Good work manual - Well-designed prompts
- Organized book stacks - Structured RAG/DB
AI is a ‘Semantic Processing Accelerator’
Once you understand this structure, AI is no longer a fearful unknown entity. It’s just another ‘component’ to plug into our system architecture.
The history of computing has been about ‘accelerating processing’.
| Hardware | What it Accelerates |
|---|---|
| CPU | Computation and logic (if-else) processing |
| GPU | Graphics and pixel processing |
| AI (LLM) | Semantics and Context processing |
We’ve struggled to process human language mechanically with code like if (text.contains("apple")). Now we have a high-performance accelerator (AI) dedicated to that ‘semantic processing’.
Conclusion
Just because it’s the AI era doesn’t mean developers need to feel compelled to build AI models themselves or understand the mathematical formulas.
Just as we can build excellent backend servers without knowing MySQL’s internal engine source code, the architectural capability of deciding where to seat this ‘smart librarian’ called AI in our service and what permissions to grant is still valid and has become even more important.
Don’t assign excessive meaning to AI. It’s not an artificial intelligence that thinks on its own and starts a rebellion—it’s a ‘Function’ and ‘Tool’ that probabilistically processes the data we input and returns the most plausible result.
Tools aren’t something to fear—they’re something to master and put to good use.
