Jev vs LLM — What’s the Difference?

One of the easiest mistakes to make when building AI systems is assuming that an LLM should make every decision.

It shouldn’t.

Sometimes the best solution is a simple if/else.

Sometimes you need an LLM.

And often, the best production system uses both.

A simple example

Imagine an insurance platform receiving this request:

“I was charged twice for the same policy payment.”

There are two very different problems hidden inside this sentence.

The first is understanding what the customer means.

The second is deciding what the system should actually do.

An LLM can be very useful for the first problem.

For example:

"I was charged twice"
        ↓
Possible intent:
Duplicate payment
        ↓
Extract information
Policy ID
Payment IDs
Transaction details

But should the LLM decide whether the customer should receive a refund?

Probably not.

That decision should usually be based on deterministic business rules.

IF

payment_1.amount == payment_2.amount
AND
payment_1.policy_id == payment_2.policy_id
AND
payment_1.status == "SUCCESS"
AND
payment_2.status == "SUCCESS"

THEN

create_refund_request()

The important distinction is this:

Understanding language and executing business logic are different problems.

Where LLMs are good

LLMs are particularly useful when the input is ambiguous, unstructured, or expressed in natural language.

For example:

"Something seems wrong with the payment I made
last week. I think I might have been charged twice."

A traditional rule-based system has to anticipate many possible variations.

An LLM can interpret the language and convert it into structured information.

For example:

{
  "intent": "duplicate_payment",
  "time_reference": "last_week",
  "confidence": 0.94
}

That structured output can then be passed to deterministic application logic.

Where traditional code is better

Once the system has structured information, ordinary software engineering techniques are often better.

For example:

• validating payment status
• checking account balances
• calculating prices
• applying eligibility rules
• checking permissions
• updating databases
• processing transactions
• enforcing compliance rules

These operations should generally be predictable.

If the same input is provided twice, you usually want the same result.

That’s exactly where deterministic code shines.

The architecture I prefer

Instead of thinking:

LLM → make every decision

think about:

User
  ↓
LLM
  ↓
Understand intent
  ↓
Structured request
  ↓
Business Logic
  ↓
Database / APIs
  ↓
Result

The LLM handles the parts where language and reasoning are useful.

The application handles the parts where correctness and predictability matter.

This is more than an AI decision

This is actually an architecture decision.

Whenever you’re adding an LLM to a system, ask:

“Does this problem actually require probabilistic reasoning?”

If the answer is no, you probably don’t need an LLM.

A simple function may be:

• faster
• cheaper
• easier to test
• easier to monitor
• easier to debug
• more predictable

And in production systems, those properties matter a lot.

The interesting part

The goal isn’t to replace traditional software with AI.

The goal is to combine them intelligently.

A production AI system might look like:

              ┌──────────────┐
              │    User      │
              └──────┬───────┘
                     ↓
              ┌──────────────┐
              │     LLM      │
              │              │
              │ Understand   │
              │ intent       │
              └──────┬───────┘
                     ↓
              ┌──────────────┐
              │ Application  │
              │ Logic        │
              └──────┬───────┘
                     ↓
        ┌────────────┴────────────┐
        ↓                         ↓
   Database                    APIs

That’s where AI becomes interesting from an engineering perspective.

Not because the LLM does everything.

But because we have to decide what the LLM should do and what it shouldn’t do.

---

Next article

The next question naturally follows:

Do we really need an LLM for every decision?

That article will go one level deeper into the architecture trade-offs between deterministic logic, traditional ML, and LLM-based systems.