AI LLMs vs Traditional Software
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In the last lesson, you learned AI RAG Basics. Now let us compare AI with normal software, because beginners often think AI apps work like normal code. They do not.
Here is the difference in one picture:
💻 Traditional Program
Traditional software means normal software where the developer writes exact instructions.
Traditional means the normal kind of software we have built for years.
For example:
- If the user clicks
Add to Cart, the app adds that product to the cart. - If the user enters the wrong password, the app shows an error.
- If the order total is 1000 and discount is 100, the app shows 900.
The app is not guessing here. It is following code written by the developer.
Input |Rules |Deterministic logic |OutputIf the input and code are the same, the output is usually the same.
That is called deterministic behavior.
Deterministic means predictable.
That makes traditional software easier to reason about for exact flows like:
- Login validation: The backend checks the saved password hash or login provider result.
- Cart totals: The app calculates price, tax, discount, and shipping using exact rules.
- Payment status: The app reads the payment provider response and marks the order as paid or failed.
- Form validation: The app checks required fields, email format, and allowed values.
- Access control: The app decides whether the user can open, edit, or delete something.
🤔 Why Traditional Software Is Still Needed
AI does not remove normal software.
In fact, AI apps need strong normal software around the model.
Exact rules
Login, payment, permissions, and totals should stay in normal backend code.
Flexible text
LLMs fit tasks where many wordings can be acceptable, like explaining, drafting, or summarizing.
Verification
Good AI apps connect model output with tests, validation, sources, and rules.
- The backend checks who the user is: The model should not decide identity from a prompt.
- The database stores real records: Orders, users, tickets, and settings need exact storage.
- The payment service handles money: Payment logic must use trusted provider responses, not generated text.
- The permission system controls access: The app must enforce what each user can see or change.
- The logging system records what happened: Logs help debug model calls, errors, and user actions.
The model helps with flexible language tasks. It should not replace the rules that must be exact.
🌍 Same App, Two Kinds of Logic
Normal code
Login, payment, cart totals, and permissions should use exact backend logic.
LLM call
Explaining, rewriting, summarizing, or drafting can be sent to a model like GPT, Claude Sonnet, Gemini, Llama, or Gemma.
App checks
The app decides which data to send to the model and checks the answer before using it for anything important.
🧠 LLM Application
An LLM application sends prompts and context to a model.
Input |Prompt |Model |Probabilistic outputProbabilistic output means the model chooses likely tokens. It may not always produce the exact same wording.
Probabilistic means based on chances.
This is useful for flexible language tasks, but risky for tasks that need exact rules.
⚖️ Deterministic and Probabilistic Together
Most useful AI products combine both.
Example support app:
| Step | Who handles it |
|---|---|
| Login check | Normal backend code checks whether the user is signed in. |
| Ticket fetch | Normal backend code loads only the tickets that user is allowed to see. |
| RAG retrieval | The app finds help document sections related to the user’s issue. |
| LLM draft | The model writes a possible answer using the ticket and help document context. |
| Validation | Normal code checks the output shape, source presence, and safety rules. |
| UI display | The screen shows the answer, sources, and maybe a warning if confidence is low. |
So the question is not “AI or code?” The real answer is “AI plus code, each doing the right job.”
🛠️ What Changes for Developers?
AI applications need different habits.
- You still write normal code for auth, billing, permissions, and data changes.
- You use the model for language, reasoning help, summarization, extraction, or generation.
- You add checks around the model output: The app should verify format, safety, sources, and business rules before trusting the answer.
- You log prompts, context, settings, and results for debugging.
- You evaluate behavior with test cases, not only unit tests.
🧪 How Testing Changes
Normal unit tests still matter, but AI needs extra checks.
- Test realistic prompts: Use examples close to what real users will ask.
- Check answer format: If the app expects JSON or three bullets, verify the model followed that shape.
- Check factual grounding: Make sure document answers use the provided source.
- Check refusal behavior: If the app lacks evidence, the model should not invent an answer.
- Compare changes: When you change model, prompt, or retrieval, check whether quality improved or broke.
This is called evaluation, and it becomes a core part of AI engineering.
| Need | Better fit |
|---|---|
| Exact tax calculation | Traditional code |
| Email rewrite | LLM |
| Checking permissions | Traditional code |
| Summarizing support tickets | LLM with review |
🧩 What You’ve Learned
- ✅ Normal software follows exact code; LLM output can vary.
- ✅ AI apps still need normal code for payments, permissions, validation, and logs.
Check Your Knowledge
Test what you learned. Pick an answer for each question, then click Check.
- 1
What does deterministic mean?
Why: Traditional code is usually deterministic for the same input and code.
- 2
What does probabilistic output mean?
Why: LLMs choose likely tokens, so wording and details can vary.
- 3
Where should access control live?
Why: Security rules must be enforced by application code.
- 4
What is a good LLM use case?
Why: Summarization is a flexible language task where LLMs can help.
🚀 What’s Next?
When we build a chatbot, the textbox is only the visible part. Behind it, we still need a backend, API key protection, model call, and answer display. Next, let us see the basic architecture of an AI app.