2025-02-19 | By Mariusz Jażdżyk
The Race for Industry-Specific Models Accelerates
Multiple paths to success are emerging. One thing is certain: data plays a crucial role in this process. However, selecting the right strategy is far from straightforward.
Many companies have already made their choices regarding the direction they want to pursue. One of the most popular approaches is fine-tuning generic models (such as LLaMA 3). However, this path comes with several constraints. Below, we outline the key differences between the fine-tuning approach and its alternative.
Fine-tuning involves adapting a pre-trained generic model to a specific domain or use case. Here are the main advantages and disadvantages:
Advantages:
High linguistic precision – The model can sound like a true expert.
Fast response time – Answers are generated quickly.
Consistent style, tone, and output format – Ensures uniformity across responses.
Disadvantages:
High costs – Full fine-tuning of a large model (e.g., LLaMA 3) can be expensive.
Catastrophic forgetting – The model may lose some general knowledge in favor of specialization.
Static knowledge – Updating requires re-tuning, making the process resource-intensive.
An alternative to fine-tuning is Retrieval-Augmented Generation (RAG), which incorporates decision-making services. Here are its key advantages and disadvantages:
Advantages:
Up-to-date information – The model dynamically retrieves data from external sources and knowledge bases.
Flexibility – Adjusting decision rules or data sources does not require modifying the base model.
Resource efficiency – Eliminates the need to retrain the model from scratch.
Disadvantages:
Higher latency – External systems can increase response time.
Risk of error propagation – Mistakes from decision services may affect responses.
Complex architecture – Integration with multiple services requires additional control and oversight.
The choice between fine-tuning and RAG depends on a company’s priorities. Organizations that require high precision, fast responses, and a specialized tone may favor fine-tuning. Meanwhile, those needing real-time updates, flexibility, and cost efficiency may prefer RAG.
This decision ultimately hinges on the trade-offs between accuracy, adaptability, and resource investment. As AI development progresses, hybrid solutions that combine both approaches may also emerge.
*Illustration from an article showcasing the fine-tuning approach, source: LinkedIn Post
Author: Mariusz Jażdżyk
The author is a lecturer at Kozminski University, specializing in building data-driven organizations in startups. He teaches courses based on his book Chief Data Officer, where he explores the practical aspects of implementing data strategies and AI solutions.
