Fine-tuning is The Holy Grail of 2026

Fine-tuning is an ML technique where a large language model is trained on smaller chunks to make its output more precise. And why is it done? Well, it’s done to allow models to learn new knowledge faster without wasting time and money.

Usually, in a data engineering company, engineers start with a pre-trained model that understands general information or data. To train it further, as per one’s requirement, our data engineers adjust its parameters. And when all of this happens, the best part is that the model betters itself in understanding various jargons, tones, and formats.

And who’s to benefit from LLM fine-tuning? If you ask us, it is businesses and developers who need specialized AI for specific tasks such as legal or medical knowledge.

Understand Why Businesses Are Gravitating Towards Fine-Tuning

We have already discussed what LLM fine-tuning is & how it works. But does it only benefit enterprises with large resources? Absolutely not! Today LLM fine-tuning is something small companies are also exploring who have the requirement to build customized budget models.

Lately it has been observed that fine-tuning is cost-friendly, since it speeds up the foundational model to bring better accuracy. Think of it like an intern developer who is given all the resources to upskill himself, thereby improving his knowledge and bringing in more precision in tasks, without the need to hire another resource for the same task.

And the best way to do this is to fine-tune sensitive data on Databricks because of its secure environment.

Let’s now quickly jump into the types of fine-tuning:

Fine-tuning goes a long way in improving a model’s relevant output, making it more effective in specialized applications. On the surface, the process can look very resource-intensive, but in the long run businesses who implement this: win.

Take a quick look at how your competitors are acing the game of fine-tuning:

• Fine-tuning involves training all layers of the neural network to yield the best results.

• Reduce computational demands by updating only selected subset parameters which are important for the model's performance.

• Additional fine-tuning adds extra layers to the model, which freezes previous training, allowing the model to train on the new layers.

• Transferable learning is another benefit that allows models to utilize their previous knowledge and implement any task asked for.

When to Fine-Tune LLMs and(When Not To)

LLM Fine-Tuning gives any model a more focused approach which helps them generate precise answers. Here are some tips compiled by our best LLM engineers, on when to use fine-tuning.

• Generate specific requirement: This in simple words mean training an already trained model to perform certain task such as sentiment analysis or text generation for a specific industry. And instead of training it from beginning; one can simply top up with new knowledge so the model understands the task better.

• Remove bias: Reduce unwanted biases in your pretrained model by fine-tuning it for balanced data.

• Compliance: Fine-tune your data on your controlled environment to prevent sensitive data leaks.

• Limited data: Beneficial for organizations working on limited data as fine-tuning models is easier than creating everything from scratch.

Therefore, LLM fine-tuning is useful when one wants a lifelong learning partner rather than an expensive one that is built from scratch. And with Spiral Mantra’s Data engineering services, this becomes easier than ever!

And When To Avoid Fine-Tuning?

Avoid fine-tuning your existing models when your larger business goal is to inject new knowledge (RAG is best for this), if data is of poor quality or you lack MLOps expertise.

Understanding Capabilities of Databricks Fine-Tuning

In a competitive business world, delivering quick and good customer experience is important. And long pauses or generic responses can put prospects off. To solve this, we at Spiral Mantra, a leading data analytics solutions company started by using RAG(Retrieval-Augmented Generation) for best customer interaction.

Now RAG usually works by gathering information from vast database of documents to provide answers. However, we noticed that despite its best efforts- it lacked. Sometimes, it provided us with irrelevant answers and even struggled to understand domain-specific terminology.

These issues made us rethink and find something that was much more stable. And during this find, we discovered Databricks.

This small yet powerful decision worked in our favour, because we were now able to generate accurate answers to our queries. And fine-tuning LLM’s gave us the power to address the complex challenges of the banking sector as well.

During our process we came across the fact that Databricks foundational model fine-tuning API supported three kinds of fine-tuning:

• Instruction-based fine-tuning: Best to fine-tune one’s model for fast response data.

• Chat completion: Best when used to fine-tune multiple chat data.

• Pre-training: This meant training one’s model with more data.

For instruction based fine-tuning, we created prompts and answers to follow specific directions. In this method we used pairs of prompts and responses to train our model to show desired output. For example, one can use this to train their model on a new task, alter its response pattern or add instructions.

For the second kind, i.e. chat completion, we trained our model to act like conversational assistants. To check its functionality we created informational interactions mimicking the banking sector.

And for the last, i.e pre-training we trained our model on initial unstructured data. When creating our training data, we used text files where we counted every file as separate data sample.

But it was not all sunshine and no hail. During our implementation, our data engineers did face some bottlenecks that we’ll be discussing about next.

Challenges in Fine-Tuning Databricks

Hallucination is a major challenge most face while fine-tuning RAG and Databricks. We noticed our models needed to be repeatedly retrained whenever new data was added. Hence, to address these challenges, we implemented a RAG combined with LLM fine-tuning.

We explored multiple ways to perform this task but found RAFT (Retrieval-Augmented Fine-Tuning) made it easier. This made our models smarter for better output on certain topics.

Who Should Use Databricks?

Considered best for enterprises and data teams who prefer unified and scalable platforms that can handle large scale processing and machine learning workloads.

Let’s break it down for you:

• Databricks is an essential asset if you are a data engineer building, and maintaining complex pipelines. With its delta live tables, this platforms makes cluster management easy easy.

• If you are a data or ML engineer, this platform provides you a unified collaborative space that can support popular ML frameworks such as TensorFlow, used to deploy models easily.

• And if you are a BI practitioner, use Databricks SQL to run high performance queries on datasheets and integrate popular tools like Power BI easily.

• The list doesn’t stop here. Best for modern CEOs, Databricks helps you tackle messy architecture easily, driving significant ROI for your business needs.

While Databricks may not be the ideal platform for small projects, organizations with specific infrastructure needs, or those with non-technical competency- this can work best for those who actually understand how to use it.

Why Choose Us?

As businesses advance, the requirement to fine-tune LLMs are rising rapidly. And to stay ahead of your competitors, one needs the right technical expertise.

Here’s where you can use our expertise. And Spiral Mantra, a leading Databricks and LLM fine-tuning company in the US is surely going to be the right choice for you.