Why You Should Not Fine-Tune Models in 2025

Why You Should Not Fine-Tune LLMs in 2025

Fine-tuning large language models (LLMs) is often a go-to strategy for adapting AI to domain-specific use cases. But in 2025, it’s time to rethink that instinct. If you’re starting a new AI project today, whether it’s building a tailored AI-enhanced tool, a smart coding assistant, or a vertical-specific AI agent, fine-tuning should likely not be your first move.

In fact, in most cases, it shouldn’t even be on your roadmap — at least not early on.

☕ Imagine This…

You’ve just bought one of the world’s best commercial espresso machines — capable of brewing barista-level coffee. But instead of testing different beans, finding the perfect grind settings, complementing it with a carefully selected milk, training staff, opening the café and serving your first customers — you decide to take things further.

You disassemble the machine, tweak the boiler internals, recalibrate the pressure valves, and swap out components to “optimize” it.

Meanwhile, someone else takes the exact same machine, plugs it in, picks a good recipe, and opens a cozy shop down the street. Customers line up. Reviews pour in. Business thrives.

This is exactly what’s often happening with AI today. Foundation models are the espresso machines — already incredibly capable. And yet, many teams fall into the trap of trying to optimize before they’ve even used what they have to its full potential.

The Organizational Trap: Decoupled AI Strategy

Part of the reason this trap is so common is organizational. AI has become such a strategic topic that many companies establish separate innovation units or dedicated AI teams to “figure it out.” While well-intentioned, this separation often introduces a blind spot: these teams are sometimes disconnected from the day-to-day business workflows, products, and tools.

Without direct exposure to actual users and practical use cases, they may default to generic AI enhancements — and fine-tuning the model often becomes the obvious (very interesting, challenging, but potentially premature) technical goal. It feels like meaningful progress, but it’s often misaligned with the needs of those who will actually use the solution.

Here are a few reasons why fine-tuning shouldn’t be your starting point — and why more flexible, faster alternatives are usually the better bet.

1. Modern LLMs Are Already Incredibly Capable

Over the last few years, we’ve witnessed an explosion in the capabilities of foundation models. The newest LLMs don’t just match older, fine-tuned counterparts — they often outperform them, even on highly specialized tasks.

Need to generate domain-specific code? Translate niche terminology? Answer questions using complex, industry-specific jargon? State-of-the-art models can already do this with remarkable accuracy — if you apply the right combination of techniques (more on this topic below).

Nowadays LLMs have a large context window giving you plenty of room for in-context learning and are very capable of following instructions in detail, and can easily be extended with “external capabilities".

So why spend big efforts collecting, cleaning, classifying and preparing a huge amount of training data and spend significant resources for fine-tuning just to marginally optimize something that already works impressively well without this heavy-weight process?

2. LLM Evolution Outpaces Your Optimization

Fine-tuning is a moving target. By the time you’ve completed your fine-tuning pipeline, a new model release may leapfrog your efforts entirely.

What was once a cutting-edge, customized model can be rendered obsolete overnight by a new release of a general-purpose LLM from a major provider. Investing heavily in tweaking or tuning may give you only a short-lived advantage — and worse, it locks you into technical debt that can be hard to justify a few months down the line.

Invest effort where future models will amplify your gains — not where they’ll make your work obsolete.

Frameworks such as Theia AI let you switch to new LLMs seamlessly, and you should prepare your whole strategy in being able to do so at any time!

3. There Are Smarter Ways to Specialize

Before even thinking about fine-tuning, you should fully explore lightweight, low-cost alternatives that often yield better results with less effort:

• Prompt Engineering: A well-crafted prompt (or sequence of prompts) can drastically change the quality and specificity of a model’s output. Structuring your instructions, framing the task, or using techniques like chain-of-thought, routing, or task splitting can unlock surprisingly effective results.
• Few-Shot and In-Context Learning: Instead of retraining the model, show it a few well-designed examples in your prompt. Modern models are adept at picking up patterns on the fly. Even if you have a very broad set of use cases, dynamic augmentation or validation can help adding the right examples for a specific request, or validate the LLMs output, e.g. in case of a domain-specific language, and let the LLM correct itself based on the validation result.
• Context Retrieval and External Capabilities: Connect the model to your own data through techniques like Retrieval-Augmented Generation (RAG). This keeps the model stateless while allowing it to dynamically access relevant domain knowledge. Equip your LLM with external capabilities or resources, like querying a database, searching the web, or executing programs, through function calling or the Model Context Protocol.

These methods don’t require massive datasets, long training runs, or expensive infrastructure. They’re flexible, fast to iterate on, more predictable, and align better with the speed at which AI is evolving. There is still quite some complexity in getting these techniques right, even without the addition of fine-tuning models underneath.

4. UX Is Often More Important Than Raw LLM Power

LLMs are only half the story. The other half — often overlooked — is how users interact with them.

A mediocre model with great UX in a tool or IDE will often outperform a state-of-the-art model with poor usability and integration in the users’ everyday tools. That’s because productivity, trust, and usefulness hinge not just on what the model can do, but on what users actually get out of it.

The best AI systems in 2025 are not just technically smart — they’re ergonomically smart. They enable:

• Efficient iteration and feedback loops with the human user
• Seamless integration and leverage in tools users’ are already familiar with
• Transparent understanding of AI suggestions in context of the users’ workflow
• Natural, low-friction interfaces that are not just correct, but enable direct action
• Opportunities for human-AI collaboration

Nailing this UX can help you circumvent the remaining limitations of today’s models — without the need for fine-tuning.

5. Users Are Even More Important

While great UX is key, even the best-designed AI features can fail without the right user enablement. Integrating AI into enterprise workflows is not just a technical challenge — it requires deliberate changes to how teams work. This goes far beyond adding another spreadsheet or new feature. In many cases, AI integration demands a fundamental shift in methodology, especially in central processes like coding, engineering, or product development.

To truly unlock the potential of AI, users must be empowered with both the knowledge and mindset to work effectively with AI tools. This includes training on how to interact with AI, how to adapt workflows to include AI-driven decision-making, and how to continuously validate AI results. The transformation is not optional — it’s essential for achieving real productivity gains and long-term success.

At EclipseSource, we support this transformation with training, strategic guidance, and tailored methodology consulting. Whether you’re onboarding developers, evolving engineering practices, or designing AI-native workflows, our services are built to ensure your team thrives in an AI-augmented future.

6. Fine-Tuning Is a Cost Trap

Fine-tuning sounds like the “serious” or “professional” route to customizing AI. But it’s also:

• Expensive
• Time-consuming
• Technically complex
• Hard to maintain
• Difficult to predict success
• Often unnecessary

It draws attention, resources, and engineering time away from areas where you could get faster wins: better prompt design, smarter workflows, tighter integrations, real-world usage testing and helping your users to adopt the innovation.

And when these efforts are driven from centralized AI teams detached from the actual workflows and end users, the results risk being irrelevant — or at least far less impactful than they could be.

Bonus Tip: Publish Your Domain Knowledge Early

If your domain or area of expertise is not yet well-covered by today’s large language models, consider publishing it — openly and early. Maybe you’re working in a niche industry, using internal codebases, or developing a proprietary toolchain. By making parts of your domain knowledge publicly available — such as code examples, specifications, documentation, or workflows — you increase the chance that future LLMs will natively include this information in their parametric knowledge.

This doesn’t mean you have to give away your competitive edge. Strategic open-sourcing or publishing of non-sensitive artifacts can help bootstrap the ecosystem — and potentially benefit your own AI adoption later on. After all, the next generation of LLMs may surprise you by “just knowing” what you’ve made visible to the world.

In a Nutshell: Integration Before Optimization

Get AI Tools in Users’ Hands Early
One of the biggest opportunities — and challenges — is getting immediate feedback from the domain experts themselves. Early hands-on use not only accelerates learning cycles and improves solution quality, but also increases long-term adoption. Involving domain experts from the beginning ensures that your AI tooling evolves in sync with real-world needs and constraints.

Use our Strategic Support to Make It Work
At EclipseSource, we specialize in helping teams make AI truly work for your domain experts. Our experienced consultants can dive deep into your project’s structure, your domain-specific requirements, and the tools you’ve adopted. We help you optimize and augment both the tools and your processes, ensuring that AI becomes a productive and aligned part of your development workflow.

👉 Learn how to empower your teams with AI-enhanced coding and AI-native software engineering

For more advanced scenarios — such as domain-specific environments, complex toolchains, or highly customized development workflows — we also offer expertise in building tailored AI-native tools. Whether you’re developing tailored extensions for Eclipse Theia or VS Code, or crafting entirely new cloud-based or web-based solutions, we support you with consulting and implementation services that bring AI to the core of your tools and platforms.

👉 Services for building AI-powered tools and IDEs

Stay Close to Where the Value Is
Don’t isolate your AI initiative from the rest of your business. Keep AI efforts embedded within the teams who build and use your tools. Let them experiment. Let them iterate. And most importantly, let them lead the charge in discovering where AI can deliver real, measurable value — using today’s models, not tomorrow’s hypotheticals.

Start lean. Move fast. Empower your experts.
And only fine-tune once you know it’s worth it.

👉 Contact us to find the perfect AI-driven solution for your projects!

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