Is LLM finetuning always the solution to improve an AI SaaS?

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LLM integrations are growing in popularity in the SaaS industry and that puts extra emphasis on the AI model’s performance as a differentiator for an AI SaaS company’s product.

With that in mind, when building an AI SaaS we can’t overlook the need to continuously improve its performance. Whether that’s through LLM finetuning, or by opting for a different LLM model altogether.

Training an LLM with specific datasets to be better suited for your use case is a good way of optimising its performance but it comes with its resource costs in time, labour, and expenses.

So in this article, we’ll be exploring the different approaches we can take to LLM finetuning and the best method for improving your AI SaaS performance under specific circumstances.

Challenges and Limitations of LLM Finetuning

AI SaaS teams can of course opt for LLM finetuning by further training an LLM with specific datasets, but the ROI will largely depend on how specific your use case is and whether you have a large enough amount of data for it to make a noticeable difference to performance.

There’s risk of diminishing returns when either the datasets you have are too small to warrant LLM finetuning, or the pre-trained base model already performs adequately enough for your use case. In which case, any LLM finetuning might yield very minimal results.

On the other hand, training with too much data can lead to ‘overfitting’ where the model is so specialised that it loses its effectiveness in broader contexts. So in some cases it can be hard for certain teams to find a balance depending on their use case.

You also have to be careful when training with your own data that the AI model doesn’t produce any outputs that are biased or harmful in any way. Usually it’s ok but you have to be attentive with the data you use by ensuring there are no legal, ethical, or privacy issues with its usage for training and LLM finetuning.

When you then also factor in the need for manual maintenance efforts for continuous monitoring, retraining, calibrating responses, and managing model drift, it can start to feel overwhelming so the ROI needs to be significant.

Alternative Approaches to Improving AI SaaS

When your AI SaaS use case is hyper specific and niche then it would make sense to start finetuning an LLM with specific datasets as this will largely outperform any LLM model with basic training and generalised capabilities.

Try a Different Base Model

But for less specific use cases you might get better results and performance from simply selecting a pre-trained model better suited to your needs as a more cost effective method to LLM finetuning. Different LLM base models perform better at certain tasks. For comparisons of different LLMs based on their benchmark tests, read our previous articles here and here.

Prompt Engineering

Another way to modify your AI model’s performance is by using prompt engineering to adjust its outputs. This is a practical way to produce more relevant, accurate, and specific responses without having to change the underlying model itself. Prompt engineering can only go so far but it requires far less resources when compared to LLM finetuning through specific datasets, so this will largely depend on how much performance enhancement you need.

AI Model Optimisation Tools

This is a method that captures the best of both worlds. You can use AI analytics tools like Phospho for real-time monitoring of user interactions with your LLM to gain full visibility into its performance. You can then create tailored metrics and custom KPIs to assess the performance in the context of your specific AI SaaS and business goals.

How Phospho.ai Can Help Optimize Your AI SaaS Without Finetuning

Using AI analytics tools would provide a sensible user driven approach before immediately resorting to resource extensive LLM finetuning.

1. Real-Time Monitoring - this lets you track and log user inputs to identify issues or trends for immediate insights into the performance of your AI SaaS and how users are engaging with it.
2. User Feedback Linking - collect, attach annotations, and analyze user feedback in context to make targeted improvements toward the AI’s responses.
3. Custom KPIs (for any use cases) - create your own KPIs and custom criteria to ‘flag’ or alert for edge cases, and label whether it was a successful or unsuccessful interaction.
4. Continuous Evaluation - use our automatic evaluation pipeline that runs continuously to keep improving your model’s performance without modifying the underlying base LLM.
5. Easy Integration - simply add Phospho to your tech stack with any popular tools and languages like JavaScript, Python, CSV, OpenAI, LangChain, and Mistral. For integration with our API for real-time logging of user interactions you can see our docs here.

Conclusion: Choosing the Right Approach for AI SaaS Improvement

LLM finetuning can be a sensible method of improving your AI SaaS performance, but as we’ve explored it comes with its challenges. Therefore, it’s important to evaluate the ROI with the different approaches we’ve discussed in this article.

For example, it might be a more cost efficient decision to just opt for a better suited base LLM for your use case as some perform better than others at certain tasks. But for very niche use cases that require training with specific datasets it can be a good decision.

However, in most cases you’re likely to find greater ROI and improvement from making more data driven decisions with tools like Phospho. If you’re building an AI SaaS and looking to optimise performance without the high costs and challenges of LLM finetuning, sign up to use Phospho for free here.