Fine-Tuning vs Retrieval: Do you really need fine-tuning in LLM or Foundational Model?

fine tuning vs retrieval


In the vast landscape of AI model training, two methods often cause confusion and misconceptions: fine-tuning and retrieval. Clients frequently struggle to discern the differences between these approaches, leading to misunderstandings about their potential effectiveness. This comprehensive article aims to shed light on the subtle nuances that set fine-tuning and retrieval apart, analyze their respective pros and cons, and provide valuable insights into making well-informed decisions tailored to specific requirements.

Our experience has convinced us that, except from some specific situations, the Retrieval process is preferable to the Fine Tuning process. Let us understand the reasons why by starting with their comparison.

A Clear-Cut Definition

When clients seek to enhance their chatbots or AI models, they sometimes blur the lines between fine-tuning and customization. Fine-tuning involves retraining an existing model, like GPT or an open-source alternative like LLAMA, utilizing client-specific data to refine its conversational abilities. On the contrary, retrieval is a different paradigm altogether. It harnesses client data to extract relevant text fragments, utilizing them to generate responses without necessitating the retraining of the entire model.

Dispelling the Myths Surrounding Fine-Tuning

A prevailing misconception often encountered in business circles is that fine-tuning equates to complete model personalization achieved through rigorous data training. However, the reality is more nuanced. While fine-tuning does offer several benefits, it does not guarantee holistic comprehension or complete memorization of new information due to the opaque nature of the model’s learning process.

Fine-Tuning: The Pros and Cons

OpenAI released fine-tuning for gpt-3.5-turbo. Let’s discuss some pros and cons of fine-tuning!


Fine-tuning is a powerful tool for specialized task training. Whether it’s converting unstructured text into structured formats like Excel sheets or performing other specific functions, this approach excels in yielding task-specific results.


  • Limited Memory Retention: Fine-tuning’s Achilles’ heel lies in its inability to effectively retain information, making it unsuitable for tasks that require extensive memory resources.
  • High Costs and Resource Requirements: The demands of fine-tuning extend beyond the virtual realm. It requires significant GPU power, translating into elevated financial investments in both hardware and/or LLM provider fees.
  • Data Processing Complexity: The fine-tuning process entails intricate data processing efforts, contributing to increased consumption of both time and resources.
  • Flexibility Challenges: Introducing a new version of a fine-tuned model is akin to starting from scratch, hindering the iterative enhancement process.
  • Slower Progress: The time-intensive nature of fine-tuning slows down the model development process, impacting swift project completion.

Retrieval: A Compelling Alternative

Enter retrieval, an alternative approach that presents its own set of advantages:

  • Cost-Effectiveness: Retrieval offers an economical solution. Its primary expenses are tied to generating and storing embeddings, making it a budget-friendly option.
  • Scalability: Expanding the knowledge base of a retrieval-based system is remarkably straightforward. New vector stores can be seamlessly added without requiring a full retraining process.
  • Speed and Efficiency: Retrieval-based systems boast rapid deployment, bypassing the time-consuming ordeal of fine-tuning.

Making the Right Choice: Evaluating the Approach

When presented with a new client seeking model training, it’s crucial to discern the most suitable course of action:

  • Fine-Tuning Considerations: Fine-tuning, despite its merits, may not always be the ideal initial choice due to its inherent limitations, associated costs, and time-intensive nature.
  • Retrieval as a Pragmatic Alternative: Retrieval emerges as a practical alternative for clients in pursuit of cost-effective, scalable, and efficient solutions.

An Informed Path Forward

In the realm of AI model training, comprehending the intricate differences between fine-tuning and retrieval is a prerequisite. While fine-tuning undeniably has its strengths, the associated constraints and costs underscore its reduced versatility. On the other hand, retrieval emerges as an appealing avenue for businesses seeking efficient, budget-friendly, and scalable knowledge expansion. Knowing the strengths and weaknesses of both methods, Mystral.AI navigates the complex scenery of AI model development with clarity and confidence, ultimately producing AI models that perfectly align with your specific needs and goals.

Lessons Learned

At Mystral.AI, we’ve observed that most of our clients express the need to build chatbots that leverage their existing data. Interestingly, even when dealing with structured data, the underlying structure is often well-defined, such as in SQL databases. A noteworthy case in point involves one of our early clients who invested a significant amount of effort and budget into fine-tuning the GPT-3 model using proprietary data. Unfortunately, this endeavour did not yield a substantial improvement in the overall response quality. In contrast, our approach took a different route. Within a week, we transformed data into embeddings, constructing a foundational retrieval-based Large Language Model (LLM) chain. The marked improvement in response quality underscores the potential of retrieval-based methods, even when dealing with structured or proprietary data. This underscores the impact of efficient data preparation and skilled implementation on AI interactions, surpassing the benefits of extensive fine-tuning. This core understanding propels our strategy at Mystral.AI, empowering us to deliver solutions that consistently meet client expectations and precisely align with their goals.