LLMs Brought a New Kind of Software

LLMs Are a New Kind of Software

We are going to presents a comparative view of Traditional Software vs LLMs (Large Language Models) to highlight how LLMs represent a fundamental shift in the software paradigm.

Comparison Table

Traditional Software LLMs (Large Language Models)
Deterministic Non-deterministic
Fast Slow
Cheap Expensive
Rigid Flexible
If → then Reasoning

Detailed Explanation

1. Deterministic vs Non-deterministic

• Traditional Software follows a fixed, rule-based approach. Given the same input, it always produces the same output.
• LLMs are probabilistic. Their outputs can vary for the same input because they are based on learned patterns from data rather than hard-coded rules.

2. Fast vs Slow

• Traditional programs execute instructions quickly because they’re optimized and compiled to run directly on machines.
• LLMs involve complex computations (e.g., matrix multiplications in neural networks), often requiring GPUs or TPUs, which can make them slower, especially for larger prompts or models.

3. Cheap vs Expensive

• Once built, traditional software is inexpensive to run at scale.
• LLMs are resource-intensive and require significant compute power, making them expensive to run, especially at high volume or with low latency requirements.

4. Rigid vs Flexible

• Traditional software is inflexible: it needs explicit updates for new logic or edge cases.
• LLMs are adaptable and can respond to a broad range of tasks (e.g., translation, coding, summarization) without being explicitly programmed for each.

5. If → Then vs Reasoning

• Traditional software logic is hard-coded using conditional statements.
• LLMs can “reason” based on their training — they generalize from massive data and can infer context or patterns to provide intelligent outputs, mimicking reasoning.

Conclusion

This comparison emphasizes the paradigm shift from rule-based programming to data-driven intelligence. LLMs open new possibilities where software can handle fuzzy, open-ended, and unstructured tasks — something traditional software struggles with — making them revolutionary in fields like AI assistance, content generation, and natural language interaction.

Certainly! Here’s a practical example demonstrating both Traditional Software (Deterministic) and LLM-based (Non-Deterministic) approaches for solving a simple task: Intent Classification from user input.

🧠 Use Case: Detecting User Intent (e.g., Greeting, Order, Complaint)

1. Traditional Software Approach (Deterministic)

def classify_intent(text):
    text = text.lower()
    if "hello" in text or "hi" in text:
        return "Greeting"
    elif "order" in text or "buy" in text:
        return "Order"
    elif "not working" in text or "problem" in text:
        return "Complaint"
    else:
        return "Unknown"

# Test
print(classify_intent("Hi there"))               # Greeting
print(classify_intent("I want to buy a laptop"))  # Order
print(classify_intent("My device is not working"))# Complaint

🔹 Deterministic: Same input = same output
🔹 Rigid: Needs explicit if-else for every condition
🔹 Fast and Cheap

2. LLM-based Approach (Non-Deterministic)

import openai

openai.api_key = "your-api-key"
def classify_intent_with_llm(text):
    prompt = f"What is the user's intent in the following message?\nMessage: \"{text}\"\nIntent:"
    
    response = openai.Completion.create(
        engine="gpt-3.5-turbo-instruct",  # Or any available model
        prompt=prompt,
        max_tokens=10,
        temperature=0.3
    )
    
    return response.choices[0].text.strip()
# Test
print(classify_intent_with_llm("Hi there"))                # Greeting
print(classify_intent_with_llm("I want to buy a laptop"))   # Order
print(classify_intent_with_llm("My device is not working")) # Complaint

🔹 Non-Deterministic: May vary slightly each time
🔹 Flexible: Can handle unseen or ambiguous phrasing
🔹 Expensive & Slower

Here’s a practical example demonstrating both Traditional Software (Deterministic) and LLM-based (Non-Deterministic) approaches for solving a simple task: Intent Classification from user input.

🧠 Use Case: Detecting User Intent (e.g., Greeting, Order, Complaint)

1. Traditional Software Approach (Deterministic)

def classify_intent(text):
    text = text.lower()
    if "hello" in text or "hi" in text:
        return "Greeting"
    elif "order" in text or "buy" in text:
        return "Order"
    elif "not working" in text or "problem" in text:
        return "Complaint"
    else:
        return "Unknown"

# Test
print(classify_intent("Hi there"))               # Greeting
print(classify_intent("I want to buy a laptop"))  # Order
print(classify_intent("My device is not working"))# Complaint

🔹 Deterministic: Same input = same output
🔹 Rigid: Needs explicit if-else for every condition
🔹 Fast and Cheap

2. LLM-based Approach (Non-Deterministic)

import openai

openai.api_key = "your-api-key"
def classify_intent_with_llm(text):
    prompt = f"What is the user's intent in the following message?\nMessage: \"{text}\"\nIntent:"
    
    response = openai.Completion.create(
        engine="gpt-3.5-turbo-instruct",  # Or any available model
        prompt=prompt,
        max_tokens=10,
        temperature=0.3
    )
    
    return response.choices[0].text.strip()
# Test
print(classify_intent_with_llm("Hi there"))                # Greeting
print(classify_intent_with_llm("I want to buy a laptop"))   # Order
print(classify_intent_with_llm("My device is not working")) # Complaint

🔹 Non-Deterministic: May vary slightly each time
🔹 Flexible: Can handle unseen or ambiguous phrasing
🔹 Expensive & Slower

You’re absolutely right — the future is not about LLMs replacing traditional software, but about merging both into a new paradigm, often referred to as:

🌐 Agentic Software Systems / Cognitive Architectures

🔁 The Future Is Hybrid

Traditional Software 🤝 LLMs / Agentic Models Deterministic logic + Reasoning & Flexibility APIs, Databases + Language, Code, Tools Speed, Control + Adaptivity, Learning

🧠 Key Concepts Emerging:

1. Agentic Workflows
   LLMs act as agents that reason, plan, and call APIs/tools (e.g., ReAct, AutoGPT, LangGraph, CrewAI).
2. Tool-Using LLMs
   LLMs delegate precise computation to traditional tools (e.g., calculators, DBs, API calls).
3. Event-driven Agents
   Instead of “if → then”, agents can “observe → think → act”.
4. Prompt Engineering + Function Calling
   Structured prompts + calling specific functions bring control and predictability to LLMs.
5. LangChain, Semantic Kernel, Autogen, CrewAI
   Frameworks are emerging to orchestrate LLMs + code + tools into reliable systems.

📌 Future Software Engineering Stack (Agentic)

[ UI / App ]
     ↓
[ Event → LLM Agent ]
     ↓
[ Plans → Tools / APIs ]
     ↓
[ Executes → Validates → Stores ]

🧭 Think of It As:

• Traditional code = Muscle
• LLMs/Agents = Brain
• Together = Intelligent System

✅ Summary

Feature | Traditional Software | LLM-based Software
Rule-based |✔| ❌
Learns from data |❌ |✔
Same output always |✔ |❌
(depends on temperature, context) Handles fuzzy input |❌ |✔

The future of software is agentic, hybrid, modular, and tool-aware. Developers will write code plus design workflows for reasoning agents that combine the best of both worlds.