LLM vs Traditional AI Models: Understanding the Difference

#learnai #oxlo #ai

We're going to build a support ticket triage pipeline that uses a traditional TF-IDF classifier for urgency labeling and an LLM for contextual response drafting. This shows exactly where deterministic statistical models end and generative reasoning begins, and it gives you a production pattern you can deploy today.

What you'll need

Python 3.10+
An Oxlo.ai API key from https://portal.oxlo.ai
The OpenAI SDK and scikit-learn installed with pip install openai scikit-learn pandas

Step 1: Train a traditional urgency classifier

Traditional models excel at bounded, structured tasks on small datasets. I will train a scikit-learn pipeline on a handful of synthetic support tickets so the agent has a fast, deterministic urgency score before it ever calls the LLM.

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split

# Synthetic support tickets for demonstration
data = [
    ("My account is completely locked out and I cannot access payroll.", "urgent"),
    ("How do I reset my password?", "low"),
    ("The API returns 500 errors for every request.", "urgent"),
    ("Can you update my billing address?", "low"),
    ("Security breach: someone accessed my dashboard.", "urgent"),
    ("Do you have a dark mode?", "low"),
    ("All my data disappeared after the sync.", "urgent"),
    ("What are your office hours?", "low"),
    ("Integration failing with timeout on large files.", "urgent"),
    ("Thanks for the great service.", "low"),
]

df = pd.DataFrame(data, columns=["text", "label"])

X_train, X_test, y_train, y_test = train_test_split(
    df["text"], df["label"], test_size=0.2, random_state=42
)

traditional_model = Pipeline([
    ("tfidf", TfidfVectorizer(stop_words="english", max_features=100)),
    ("clf", LogisticRegression(max_iter=1000)),
])

traditional_model.fit(X_train, y_train)
print(f"Traditional classifier accuracy: {traditional_model.score(X_test, y_test):.2f}")

Step 2: Build the deterministic pre-filter

The classifier runs locally in milliseconds and never hallucinates a label. I will wrap it in a helper so the rest of the agent can treat it like a pure function.

def classify_urgency(ticket_text: str) -> str:
    """Return 'urgent' or 'low' using the traditional model."""
    return traditional_model.predict([ticket_text])[0]

Step 3: Connect the LLM through Oxlo.ai

LLMs handle tone, nuance, and open-ended reasoning that logistic regression cannot. I use Oxlo.ai's OpenAI-compatible endpoint to call Llama 3.3 70B, and because Oxlo.ai charges a flat rate per request, I can pack the full ticket context into the prompt without token math.

from openai import OpenAI

client = OpenAI(base_url="https://api.oxlo.ai/v1", api_key="YOUR_OXLO_API_KEY")

def draft_response(ticket_text: str, urgency: str) -> str:
    user_message = f"Ticket: {ticket_text}\nClassified urgency: {urgency}"
    response = client.chat.completions.create(
        model="llama-3.3-70b",
        messages=[
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": user_message},
        ],
        temperature=0.3,
    )
    return response.choices[0].message.content

The Agent's System Prompt

I keep the prompt explicit and short. The LLM is only responsible for tone and policy, not for re-classifying the ticket.

SYSTEM_PROMPT = """You are a senior support engineer.

You will receive a customer support ticket and a pre-classified urgency label from a deterministic classifier.

Your rules:
- If urgency is 'urgent', acknowledge the severity, apologize, and provide an immediate next step.
- If urgency is 'low', answer concisely and point to documentation when possible.
- Do not make up facts. If you lack detail, ask one clarifying question.
- Keep responses under 120 words.
"""

Step 4: Assemble the hybrid pipeline

Now I wire the two paradigms together. The traditional model supplies structure, and the LLM supplies language. This is the core difference in action.

def process_ticket(ticket_text: str):
    urgency = classify_urgency(ticket_text)
    reply = draft_response(ticket_text, urgency)
    return {"urgency": urgency, "reply": reply}

if __name__ == "__main__":
    tickets = [
        "I accidentally deleted my production database.",
        "Is there a way to export my monthly report as a PDF?",
    ]
    for t in tickets:
        result = process_ticket(t)
        print(f"Ticket: {t}")
        print(f"Urgency: {result['urgency']}")
        print(f"Draft: {result['reply']}\n")

Run it

Save the script as agent.py, export your key with export OXLO_API_KEY=..., and run python agent.py. You should see output similar to this:

Traditional classifier accuracy: 1.00
Ticket: I accidentally deleted my production database.
Urgency: urgent
Draft: I am sorry to hear that. This is a critical issue. Please immediately stop all writes to the instance and open a P1 incident in our status portal. Our on-call engineer will contact you within 15 minutes. Do you have a recent snapshot enabled?

Ticket: Is there a way to export my monthly report as a PDF?
Urgency: low
Draft: Yes. Navigate to Reports > Monthly, click the export icon, and select PDF. For advanced formatting options, see our docs at https://docs.example.com/reports.

Wrap-up and next steps

You now have a working hybrid agent that contrasts the two eras of AI. A traditional model gives you speed and deterministic structure, while an LLM gives you flexible, human-readable reasoning. If you want to push this further, add a confidence threshold to the classifier so uncertain tickets bypass the labeler and go straight to the LLM with a longer context window. On Oxlo.ai, that extra context costs nothing extra because of the flat per-request pricing, which makes edge-case handling simple. See https://oxlo.ai/pricing for details.