Fine-Tuning Isn’t Always Fine

"In our quest to teach machines more, we often forget how much they already know."

🚀 My First Step into AI Engineering

After spending two solid years building mobile apps as an Android Engineer, I recently made a leap toward the fascinating world of Generative AI. As part of my transition, I started digging deep into how LLMs (Large Language Models) work. And that's when I stumbled upon something that really blew my mind:

Fine-tuning can damage your model's intelligence.

Yes, you heard that right. The same fine-tuning that we often praise for "personalizing" a model can actually overwrite important neurons and make the model... well, dumber in some ways.

This blog is my first on this journey. If you're a beginner or even someone experienced who hasn’t thought much about this, I hope this gives you some food for thought.

🔀 What Fine-Tuning Really Does

When people say "we fine-tuned the model on our company data," it sounds smart and necessary. And it often is.

But here's the core idea:

Fine-tuning adjusts the model’s weights (its neurons) using new data.

These weights aren’t empty. They already encode years of diverse training on books, websites, articles, conversations, and more. Think of them like the brain of a super-experienced person. Fine-tuning is like saying:

"Hey Einstein, forget half of physics because our company has a new definition for gravity."

Sounds risky, right?

🧬 What Do We Mean by "Overwriting Neurons"?

Neurons in deep learning models are just numbers (weights) connected in layers. These weights determine how the model understands and represents knowledge.

During fine-tuning:

Your new data is fed into the model.
It tries to predict something.
It compares the prediction to the actual result.
If it's wrong, it uses backpropagation and gradient descent to adjust the weights.

This adjustment is where things go sideways.

If the new data is too specific, too narrow, or too different from what the model already knows, it may overwrite useful neurons, harming performance on other tasks.

This is why people often complain that after fine-tuning a model for their business, it starts making weird mistakes on general questions it used to handle perfectly.

🪨 An Analogy: Teaching a Genius to Forget

Imagine a child prodigy in music who has learned classical, jazz, and rock.

Now you want them to perform only Bollywood songs. So every day, you erase parts of their memory related to Bach, Coltrane, and Metallica, and replace it with Bollywood scores.

Eventually, they become excellent at Bollywood, but can't play jazz anymore.

That's what can happen when fine-tuning goes unchecked.

🔧 So What Should You Do Instead?

Instead of altering the foundational brain of the model, you can use modular techniques to add knowledge without overwriting it.

1. Retrieval-Augmented Generation (RAG)

RAG augments the model with a search system. It retrieves relevant documents from a vector database and passes them to the model at inference time.

The model remains untouched. All new knowledge lives in the external documents.

2. Adapters / LoRA (Low-Rank Adaptation)

Adapters and LoRA add small trainable layers on top of the base model. They allow custom learning while freezing the original weights.

Think of this like giving your model a new set of reading glasses, without changing its brain.

3. Prompt Engineering

If your use case can be solved by just crafting the right prompt, you don't need to change anything internally.

This is powerful when combined with tools like OpenAI's function calling or LangChain output parsers.

🎓 But Isn’t Fine-Tuning Useful?

Yes, absolutely! Fine-tuning is essential when:

You need to teach the model new behavior (e.g., legal writing, SQL code generation).
You have a lot of high-quality domain-specific data.
You can evaluate thoroughly to detect regression in other tasks.

But it's not the only hammer in your toolbox.

🔍 What Research Says

This idea isn't new. Research papers like:

... have all explored this very problem: How can we add task-specific knowledge without damaging the general-purpose capabilities of LLMs?

🙌 Final Thoughts

This blog marks the beginning of my Gen AI engineering journey. I'm still learning, still making mistakes, and still connecting the dots.

If you find something wrong, outdated, or confusing, I'd genuinely appreciate your feedback.

Because the only way to grow in this space is through curiosity, collaboration, and course correction.

"In machine learning, just like in life, you learn more by listening than overwriting."

Thanks for reading. Feel free to drop your thoughts, comments, or corrections.

🔗 References & Further Reading:

OpenAI Cookbook: Fine-tuning best practices
LoRA: https://arxiv.org/abs/2106.09685
RAG: https://arxiv.org/abs/2005.11401

Follow me on Medium& Hashnode if you're also curious about the messy, exciting and transformative world of AI Engineering!

Let this be the first of many nerdy explorations.