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May 21, 2024

RAG vs Fine-Tuning: A Comparative Analysis of LLM Learning Techniques


Artur Haponik

CEO & Co-Founder

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17 minutes

The emergence of Large Language Models (LLMs) has revolutionized the field of Natural Language Processing (NLP), demonstrating great capabilities in tasks such as text generation, language translation, question answering, and text summarization. As the popularity of LLMs continues to increase, many organizations and developers have started building advanced applications to harness the power of these models.

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However, despite having great potential, even the most powerful pre-trained LLMs may not always meet your specific needs right out of the box.

Therefore, it’s necessary to adopt techniques that enable LLMs to specialize in specific domains and tasks, thus improving their overall effectiveness, accuracy, and versatility. Among the various techniques employed to enhance the performance of LLMs, two prominent approaches have emerged as frontrunners: Retrieval Augmented Generation (RAG) and fine-tuning.

This post will provide an in-depth review of RAG vs fine-tuning, shedding light on the strengths and weaknesses of both LLM learning techniques – RAG and fine-tuning – and their overall impact on the performance and functionality of large language models.

Read in-depth analysis of RAG and fine-tuning to discover which of the two techniques best suits your project.

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What is Retrieval-Augmented Generation?

RAG, short for Retrieval-Augmented Generation, is an LLM learning technique that merges the retrieval mechanisms and generative capabilities to enhance the performance of large language models.

Generally, retrieval models are good at searching vast external knowledge bases and finding relevant information for a given prompt. Generative models, on the other hand, excel at utilizing this information to generate new text.

This hybrid approach often leads to the generation of accurate, informative, and in-context results as compared to using retrieval and generative models separately.

How Retrieval-Augmented Generation (RAG) Works

Here is a step-by-step breakdown of how Retrieval Augmented Generation works:

How Retrieval-Augmented Generation (RAG) Works

  1. Vector Database
    To implement retrieval-augmented generation (RAG), you must start by inserting the internal dataset, then creating vectors from it, and finally store these vectors in a vector database. [1]
  2. User Query/Input
    Once the vector database is set up, the user provides a query or input to the large language model. In this case, an input could be a question that needs to be answered or a statement that you need to complete.
  3. Retrieval Component
    Next, the LLM activates its retrieval component after it has received the query or input from the user. This component basically scans the vector database/large knowledge base like Wikipedia or web pages to find retrieved documents or chunks of information that are semantically similar to the user input or query. These chunks of relevant information are then used to give context to the large language model, enabling it to develop a more accurate, informative, and context-aware response.
  4. Knowledge Representation
    In this step, the retrieved documents are processed and converted into a format understandable to the LLM. This may involve creating summaries, extracting key sentences, or even generating other representations to help capture the important information.
  5. Concatenation
    Afterwards, the retrieved documents are linked to the original user query/input to provide additional context for generating responses.
  6. Text Generation
    The large language model uses its generator component to process the user input, retrieved documents, and concatenated query to generate a coherent, informative, and relevant response to the original user query/input.

RAG Use Cases

Overall, Retrieval Augmented Generation is useful in application areas that require LLMs to base their responses on large amounts of documents specific to the application’s context.

That said, here are some of the most popular RAG use cases:

Chatbots and AI Technical Support

One of the best applications of RAG-powered systems is question-answering systems. [2] Chatbots with RAG capabilities can easily pull relevant information from an organization’s instruction manuals and technical documents to help provide detailed and context-aware answers to customer queries. These systems usually enable organizations to have informative and engaging interactions with their customers.

Language Translation

Retrieval-Augmented Generation (RAG) helps improve language translating tasks by considering the context element in an external knowledge base. By considering specific terminology and domain knowledge, this advanced approach leads to more accurate language translations. This is particularly useful in technical and specialized fields.

Medical Research

RAG-powered systems provide access to up-to-date medical documents, clinical guidelines, and information that weren’t part of the LLM training dataset. As a result, these systems help medical professionals come up with accurate diagnoses and provide better treatment recommendations to their patients.

Educational Tools

Adopting RAG in LLM learning has significantly improved the educational tools available to students. Thanks to this technique, students can now access answers, explanations, and even additional context based on various study materials. This leads to effective learning and comprehension in learning institutions.

Legal Research and Document Review

Nowadays, legal professionals worldwide can rely on RAG models to streamline legal document review processes and conduct effective legal research. These models can help in analyzing, reviewing, and summarizing a wide variety of legal documents, such as contracts, statuses, affidavits, wills, and other legal documents, in the shortest time possible.

This helps significantly reduce the amount of time and effort required for legal document review, allowing legal professionals to focus on more important tasks. Using RAG systems also helps improve the accuracy and consistency of the legal document review processes.

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Benefits of RAG

Retrieval Augmented Generation offers a wide variety of benefits to LLM learning, including:

Improved accuracy

RAG allows LLMs to access and utilize vast external knowledge bases, leading to more accurate, informative, and grounded responses.

Reduced hallucinations and biases

Sometimes, LLMs trained on limited data make ‘best-guess’ assumptions and generate factually incorrect or biased responses. Fortunately, RAG prevents this problem by allowing LLMs to access factual information from external sources. This minimizes the risk of hallucinations and improves the overall accuracy of the responses.

Adaptability to new data

Retrieval Augmented Generation can easily adapt to situations and tasks where the information has changed over time. This makes it particularly useful for tasks that require up-to-date information or domain-specific knowledge. [3]

Interpretability and transparency

By using the RAG technique, the source of an LLM’s answer can easily be identified based on the referenced knowledge sources. This is particularly essential for quality assurance and handling customer complaints.


Unlike other LLM learning techniques that require large amounts of labeled training data, RAG can achieve high performance with less labeled data and resources. This makes it a more cost-effective and efficient technique for developing large language models.

What is Fine-Tuning?

Fine-tuning refers to the process of training a large language model that has already been pre-trained to help improve its overall performance. By fine-tuning, you’re simply adjusting the model’s weights, architecture, or parameters based on the available labeled data, making it more tailored to perform specialized tasks.

Read more about What is fine-tuning in NLP?

How fine-tuning works

Here is a step-by-step guide on how to go through the full fine-tuning process:

how fine-tuning works, the full fine-tuning process

  1. Pre-Train an LLM
    As you embark on a fine-tuning process, you must have a pre-trained large language model. This means you need to gather large amounts of text and code and use it to train a general-purpose LLM. The main purpose of pre-training an LLM is to help it learn fundamental language patterns and relationships. Most importantly, a pre-trained LLM will have the generic knowledge and skills needed to perform various tasks, but it may not perform well on domain-specific tasks without further training.
  2. Prepare Task-Specific Data
    Proceed to collect and label a smaller dataset relevant to your desired task. Such a dataset will provide the LLM with the various types of input and output it needs to learn from to perform the desired task. Once you’ve collected and labeled the task-specific data, split it into training, validation, and test sets. [4]
  3. Reprocess the Data
    The quality and quantity of your task-specific data will impact the effectiveness of your fine-tuning process. That said, start by converting your task-specific data into a form acceptable to your LLM. Once you’re done, identify and correct any errors present in your dataset. This may involve fixing inconsistent data, removing duplicates, and handling outliers.
  4. Adjust the Layers
    A pre-trained LLM is usually made up of different layers, each responsible for extracting various parts of the input data. When fine-tuning a pre-trained LLM, some layers are not updated during the training process. Such layers represent general knowledge and should always remain unchanged. On the other hand, top/later layers are modified based on the task-specific data. This allows the large language model to adapt its knowledge for the desired task.
  5. Configure the Model
    Once you’ve loaded the pre-trained model, you need to decide on the parameters for fine-tuning. These parameters may include learning rate, batch size, regularization techniques, and number of epochs based on domain knowledge. Adjusting these parameters is important for achieving your model’s best performance.
  6. Train the Model
    Feed the reprocessed data to the pre-trained LLM as input and train it using a backpropagation algorithm. Since you’re training an already pre-trained model, you’ll often need fewer epochs than if you were training a model from scratch. The training process will help adjust the weights and biases of the fine-tuned layers to help minimize errors between the model’s predictions and the desired output. The training process is supposed to continue until the LLM achieves satisfactory performance on the desired task. It’s important to continuously monitor the model’s performance on the validation dataset to prevent overfitting and decide when to stop the training process or make necessary adjustments to the data.
  7. Evaluate Performance
    Once the fine-tuning process is complete, evaluate the LLM’s performance on an unseen test dataset. This ensures the model has learned the desired patterns and can easily adapt to new patterns. You can use various metric scores to assess the performance of the LLM, including the BLEU score, ROUGE score, or even human evaluation. [5]
  8. Iteration and Deployment
    Based on the evaluation results, repeat the above steps to help improve the model’s performance. Once the LLM achieves satisfactory performance, it can be deployed to a specific application to perform the desired task.

Fine-tuning use cases

Large Language Models (LLMs) can be fine-tuned for a wide variety of applications, including:

Sentiment analysis

Although pre-trained LLMs can understand human language quite well, they don’t always do a great job when it comes to analyzing the tone and sentiment behind a particular text. Fine-tuning an LLM can help improve its capabilities in determining the attitude and emotion expressed in a given text.

This way, the LLM will be able to deliver the most accurate sentiment analysis from online reviews, customer support chat transcripts, and even social media comments. With the help of accurate sentiment analysis, organizations are able to make more informed decisions regarding their products and customer service to boost customer satisfaction.

Named-entity recognition(NER)

A generic LLM is more likely to stumble when it encounters specialized vocabulary in a domain-specific text. On the other hand, fine-tuning will allow an LLM to easily recognize specialized entities such as legal jargon or even specialized medical terms, thus improving its NER capabilities.

An LLM with NER capabilities can easily identify and categorize key elements such as names, places, and dates within a given text. This is vital for converting unstructured data to structured data.

Personalized content recommendation

Providing content suggestions that match a customer’s specific needs creates a sense of personalization and makes the customer feel understood and valued. This is particularly important when it comes to news and entertainment content. Unfortunately, news and entertainment companies cannot rely on generic LLMs to recommend personalized content to their users. This is because such models may recommend content based on general popularity instead of the customer’s preference.

By fine-tuning a pre-trained LLM, you can make it better suited to analyze and understand the customers’ unique preferences and needs. This ensures that the model only suggests entertainment content or news articles that tightly align with their preferences, thus keeping them more engaged on your platform.

Benefits of LLM fine-tuning

The following are some of the benefits of fine-tuning as an LLM learning technique:

Less training data required

Since a pre-trained LLM already has generic language and skills, the fine-tuning process requires less training data. In fact, a pre-trained LLM initially trained on 1 trillion tokens can effectively be fine-tuned on only a few hundred tokens. [6] As a result, fine-tuning a pre-trained LLM is much faster than training a model from scratch.

Improved accuracy

LLM fine-tuning allows it to understand the specialized aspects of a particular domain or task. As a result, fine-tuned LLM is more likely to generate more accurate and relevant responses compared to a generic model.

Increased robustness

LLM fine-tuning usually exposes an LLM to more examples and less common scenarios present in the domain-specific dataset. This allows the LLM to complete a wide variety of domain-specific tasks without producing erroneous or inaccurate outputs.

RAG vs. fine-tuning

Retrieval-Augmented Generation (RAG) and fine-tuning are two completely different approaches to building and using Large Language Models (LLMs). That said, there are various factors you need to consider when choosing between RAG and LLM fine-tuning as your preferred LLM learning technique.

They include:

Dynamic vs. Static

Generally, RAG performs exceptionally well in dynamic settings. This is because it regularly requests the most recent data from external knowledge bases without the need for frequent retraining. This ensures that the information generated by RAG-powered models is always up-to-date.

In contrast, fine-tuned LLMs often become static snapshots of their training datasets and easily become outdated in scenarios involving dynamic data. Additionally, fine-tuning isn’t always reliable since it sometimes doesn’t recall the knowledge it has acquired over time.


RAG-powered LLMs have a hybrid architecture that combines a transformer-based model with an external knowledge base. Such a base allows you to efficiently retrieve relevant information from a knowledge source like company records, a set of fundamental documents, or a database.

On the other hand, LLM fine-tuning often begins with a pre-trained LLM that is further trained on a task-specific dataset. In most cases, this architecture remains unchanged, with only adjustments made to the LLM’s weights or parameters to enhance its performance for the desired task.

Training data

RAG LLMs often rely on a combination of supervised and labeled data that demonstrates how to properly retrieve and utilize relevant external information. This explains why RAG-powered models can easily handle both retrieval and generation.

In contrast, fine-tuned LLMs are trained using a task-specific dataset that mostly comprises labeled examples that match the desired task. Fine-tuned models are adapted to perform various NLP tasks, but aren’t built for information retrieval.

Model customization

RAG models mainly focus on information retrieval and may not automatically adapt their linguistic style or domain specialization based on the information obtained from an external knowledge base. This LLM learning technique excels at incorporating external relevant information but may not fully customize the LLM’s writing style or behavior.

Fine-tuning, on the other hand, allows you to adjust an LLM’s behavior, domain-specific knowledge, and writing style to align with specific nuances, terminologies, or even tones. Basically, fine-tuning offers full model customization with respect to writing styles or expertise areas.


Generally, RAG is less prone to hallucinations and biases since it grounds each response generated by an LLM in retrieved documents/evidence. Since it generates information from retrieved data, it becomes nearly impossible for it to come up with fabricated responses due to limited training data.

Fine-tuning processes, on the other hand, can help reduce the risk of hallucinations by simply focusing on domain-specific data. However, fine-tuned models are still likely to generate inaccurate or erroneous responses when faced with unfamiliar queries.


Although RAG excels in generating up-to-date responses and minimizing the risk of hallucinations, its accuracy may vary based on the domain or task at hand. On the other hand, fine-tuning focuses on enhancing a model’s domain-specific understanding, which often leads to more accurate responses and predictions.


RAG provides more transparency by splitting response generation into different stages, providing valuable information on data retrieval, and enhancing user trust in outputs. In contrast, fine-tuning functions like a black box, obscuring the reasoning behind its responses.


RAG requires less labeled data and resources than fine-tuning processes, making it less costly. Much of RAG expenses often go into setting up embedding and retrieval systems.

In contrast, fine-tuning requires more labeled data, significant computational resources, and state-of-the-art hardware like high-performance GPUs or TPUs.[7] As a result, the overall cost of fine-tuning is relatively higher than RAG.


RAG is relatively less complex as it only requires coding and architectural skills. Fine-tuning, on the other hand, is more complex as it requires an understanding of NLP, deep learning, model configuration, data reprocessing, and even evaluation.

RAG vs. fine-tuning: Final thoughts

The main differences in RAG vs fine tuning lie in complexity, architectural design, use cases, and model customization. That said, the choice between the two LLM learning approaches should be based on the available resources, the need for customization, and the nature of the data. This way, you can tailor your preferred LLM learning technique to their specific needs.

It’s important to note that RAG and fine-tuning are not rivals. Although both LLM learning approaches have their strengths and weaknesses, combining them may be the best solution for your organization. Fine-tuning a model for a particular task and then enhancing its performance with retrieval-based mechanisms may be exactly what you need for a successful LLM project.

RAG and fine tunning – FAQ

What techniques enhance LLM performance for specialized tasks?

Two prominent techniques for enhancing LLM performance are RAG and fine-tuning. Both methods help adapt LLMs to specific tasks and domains, improving their utility and accuracy. Both, RAG and fine tuning, require additional data to improve model performance. RAG uses external knowledge bases for retrieval, while fine-tuning uses labeled task-specific datasets.

What is Retrieval-Augmented Generation (RAG)?

RAG combines retrieval mechanisms with generative capabilities, allowing LLMs to access external knowledge bases to generate more accurate and contextually relevant responses.

What are common use cases for RAG?

  • Chatbots and AI Technical Support: Answer customer queries using organizational knowledge bases.
  • Language Translation: Improve translations with domain-specific context.
  • Medical Research: Provide up-to-date medical information.
  • Educational Tools: Enhance learning with detailed, context-based answers.
  • Legal Research and Document Review: Streamline legal document analysis and review.

RAG vs fine tuning. How do RAG and fine-tuning compare?

RAG and fine-tuning differences:

  • Dynamic vs. Static: RAG excels in dynamic environments with up-to-date information, while fine-tuning may result in static models.
  • Architecture: RAG uses a hybrid model with retrieval capabilities, while fine-tuning adjusts a pre-trained LLM for specific tasks.
  • Training Data: RAG uses supervised data for retrieval and generation, while fine-tuning relies on task-specific labeled data.
  • Model Customization: Fine-tuning offers more customization for writing style and behavior, while RAG focuses on information retrieval.
  • Hallucinations: RAG is less prone to hallucinations, but fine-tuning can reduce hallucinations with domain-specific data.
  • Accuracy: Fine-tuning often provides higher accuracy for specialized tasks.
  • Transparency: RAG offers greater transparency in response generation.
  • Cost: RAG is generally more cost-effective than fine-tuning.
  • Complexity: Fine-tuning is more complex, requiring deeper knowledge of NLP and model training.

Can RAG and fine-tuning be combined?

Yes, combining RAG and fine-tuning can leverage the strengths of both techniques, offering a robust solution for developing highly effective and customized LLM applications.

This article is an updated version of the publication from Dec 18, 2023.


[1] Vector Database in LLMs and Search. URL: Accessed on December 10, 2023
[2] Building a Question Answering System Using LLM. URL: Accessed on December 10, 2023
[3] Domain Knowledge. URL: Accessed on December 12, 2023
[4] Train Validation and Test Sets. URL: Accessed on December 12, 2023
[5] Understanding BLEU and ROUGE for NLP Evaluation. URL:, Accessed on December 12, 2023
[6] LLM AI Tokens. URL: Accessed on December 12, 2023
[7] TPU vs GPU: Pros and Cons. URL: Accessed on December 13, 2023


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