Samuel Ochaba

Posted on Dec 11

Base LLMs vs Instruction-Tuned LLMs: Understanding the Architecture Behind ChatGPT and Claude

#llm #ai #python #machinelearning

If you've been building with LLMs lately, you've probably noticed something interesting: the models powering ChatGPT, Claude, and similar tools behave very differently from raw language models. Let's unpack why.

The Two-Stage Architecture

Modern conversational AI follows a two-stage training pipeline:

Pre-training → Base LLM (Foundation Model)
Post-training → Instruction-Tuned LLM (Chat Model)

Understanding this distinction isn't just academic—it directly impacts how you architect AI applications, write prompts, and debug unexpected behavior.

Base LLMs: The Foundation Layer

What They Are

Base LLMs are trained via causal language modeling on massive corpora (CommonCrawl, books, code repositories, etc.). The training objective is straightforward:

Given tokens [t1, t2, ..., tn], predict tn+1

That's it. Maximum likelihood estimation over next-token prediction.

Behavior Characteristics

Here's where it gets interesting. Give a base model this prompt:

prompt = "Q: What is the capital of France?\nA:"

You might expect "Paris", but you could get:

Q: What is the capital of Germany?
A: Berlin

Q: What is the capital of Italy?

Why? Because the model has seen similar Q&A lists in training data and is continuing the pattern, not answering your question.

Code Example

# Using a base model (e.g., GPT-2)
from transformers import GPT2LMHeadModel, GPT2Tokenizer

tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')

prompt = "def fibonacci(n):"
inputs = tokenizer(prompt, return_tensors='pt')
outputs = model.generate(**inputs, max_length=50)

print(tokenizer.decode(outputs[0]))
# Might complete the function, or might continue with 
# unrelated code snippets it's seen before

When to Use Base LLMs

Fine-tuning for domain-specific tasks (legal, medical, code)
Research on model behavior and capabilities
Building custom alignment approaches
Cost-sensitive applications where you can craft prompts carefully

Instruction-Tuned LLMs: The Application Layer

The Post-Training Process

Instruction tuning typically involves:

Supervised Fine-Tuning (SFT): Training on instruction-response pairs
RLHF (Reinforcement Learning from Human Feedback): Using a reward model to align outputs with human preferences
Constitutional AI (optional): Additional safety and principle-based training

Architecture Changes

The interesting part: the model architecture remains identical. We're not adding layers or changing attention mechanisms—we're optimizing the same parameters to produce different behavior.

# Conceptual training loop for SFT
for instruction, response in instruction_dataset:
    # Standard supervised learning
    loss = model.compute_loss(instruction, response)
    loss.backward()
    optimizer.step()

Behavior Comparison

Same prompt, different model:

# Base LLM
prompt = "Explain recursion"
# Output: "in 500 words or less. Include examples from 
# computer science and mathematics..."

# Instruction-tuned LLM
prompt = "Explain recursion"
# Output: "Recursion is a programming technique where 
# a function calls itself to solve a problem..."

Production Considerations

Advantages:

Predictable, instruction-following behavior
Better out-of-the-box UX for end users
Reduced prompt engineering overhead
Built-in safety guardrails

Trade-offs:

Can be overly verbose (the "AI essay problem")
May refuse edge-case requests due to conservative safety training
Potential performance regression on specific tasks
Sycophantic behavior (agreeing with user even when wrong)

Practical Implementation Patterns

Pattern 1: Direct API Usage

import anthropic

client = anthropic.Anthropic(api_key="your-key")

# Instruction-tuned model expects conversational format
message = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Refactor this code: [code]"}
    ]
)

Pattern 2: Few-Shot Prompting

# Works better with instruction-tuned models
prompt = """
Classify sentiment:

Text: "This movie was amazing!"
Sentiment: Positive

Text: "Waste of time"
Sentiment: Negative

Text: "It was okay, nothing special"
Sentiment:"""

# Model understands the task structure and completes it

Pattern 3: System Prompts

# Leveraging instruction-following capabilities
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {
            "role": "system", 
            "content": "You are a code reviewer. Provide concise feedback."
        },
        {
            "role": "user", 
            "content": "Review this function: [code]"
        }
    ]
)

Debugging Common Issues

Issue: Model Not Following Instructions

Possible causes:

Using a base model when you need instruction-tuned
Prompt format doesn't match training data structure
Context window exceeded (model "forgets" earlier instructions)

Issue: Over-Refusals

Common with instruction-tuned models:

User: "Write a function to delete files"
Model: "I can't help with that as it could be dangerous..."

Solution: Rephrase or provide context:

User: "Write a Python function for a file cleanup utility 
that deletes temporary files with user confirmation"

The Bottom Line for Developers

Building a chatbot/assistant? → Use instruction-tuned models
Fine-tuning for specific tasks? → Start with base models
Prototyping quickly? → Instruction-tuned saves time
Maximum control and customization? → Base models offer flexibility

DEV Community