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:

1. Pre-training → Base LLM (Foundation Model)
2. 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:

1. Supervised Fine-Tuning (SFT): Training on instruction-response pairs
2. RLHF (Reinforcement Learning from Human Feedback): Using a reward model to align outputs with human preferences
3. 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

Further Reading

• InstructGPT Paper
• Constitutional AI
• RLHF Explained

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What’s your experience working with different model types? Any gotchas I missed? Drop your thoughts in the comments! 👇

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