Ai Engineering Core / Understanding and Competing LLMs

Understanding LLM Types

🧠 The Evolution of LLMs

LLMs have evolved in stages based on the tasks they were trained to perform.

The progression looks like this:

📦 Base Models — Just Predicts Text

A Base Model is the most fundamental type of language model. A base model does one thing only:

Predict the most likely next word

That's all it does. It doesn't:
❌ Hold conversations naturally
❌ Follow instructions
❌ Think step-by-step
❌ Reason deeply

Think of it like 📱 phone keyboard suggestions — just smarter.

Used for: ✅ Fine-tuning ✅ Research ✅ Building custom models
Not great for normal conversations ❌

💬 Chat Models — Talks Naturally

A chat model is a base model additionally trained to hold conversations, follow instructions, and maintain context.

Trained using conversation patterns:

Uses RLHF (Reinforcement Learning from Human Feedback) — humans rank responses, better ones get rewarded.

Also introduced structured prompts:

  • 🔧 System prompt — defines behavior ("You are a helpful assistant")

  • 💬 User prompt — the question

  • 🤖 Assistant response — the answer

Used for: ✅ Chatbots ✅ Writing ✅ Customer support
Weakness: ❌ Struggles with deep reasoning

🧠 Reasoning Models — Thinks Before Answering

Instead of jumping straight to an answer, reasoning models work through steps first:

Normal:    Question → Answer
Reasoning: Question → Thinking → Answer

Inspired by a simple trick — adding "Think step by step" to prompts dramatically improved results. Researchers then trained models to reason this way by default.

Used for: ✅ Math ✅ Coding ✅ Logic ✅ Deep analysis
Weakness: ❌ Slower and more expensive

⚡️ Hybrid Models — Best of Both Worlds

Hybrid models combine chat + reasoning and decide dynamically how much thinking a task needs.

"Hi!"                         →  Instant reply 💬
"Design a microservices arch"  →  Deep reasoning 🧠

Examples: GPT-5, Gemini 2.5 Pro

💡 Bonus: Budget Forcing

Researchers discovered that forcing a model to think longer improves accuracy. Even inserting the word "Wait" into reasoning makes the model reconsider its answer.

Sounds silly — but it works! 😄

📊 Quick Comparison

Feature

Base

Chat

Reasoning

Hybrid

💬 Conversation

❌

✅

✅

✅

🧠 Deep Reasoning

❌

Limited

✅

✅

⚡ Speed

Fastest

Fast

Slow

Adaptive

💰 Cost

Low

Medium

High

Balanced

🎯 Which Model Should You Use?

Task

Model

💬 Chatbots & writing

Chat Model

🧮 Math & coding

Reasoning Model

⚖️ Mixed workloads

Hybrid Model

🔬 Custom training

Base Model