A Practical Rag vs LLM Guide for Modern AI Applications

Generative AI can feel like a treasure trove stuffed with goodies. Yet, to discover the right gem for your needs, you must first sort through the various approaches and models. For instance, should you use a retrieval-augmented generation (RAG) or a large language model (LLM)? How does the RAG vs LLM debate even break down? If you’re left scratching your head, you’re not alone. The difference between RAG and LLM is significant, and both options have their strengths. This blog will help you confidently choose and implement the best generative AI approach—RAG or multimodal LLM—for your specific use case, ensuring optimal performance, relevance, and efficiency in your GenAI applications.

Lamatic's solution, Generative AI tech stack, is a valuable tool to help you achieve these objectives and simplify the decision-making process around RAG vs LLM.

What is Retrieval-Augmented Generation (RAG)?

Retrieval-augmented generation, or RAG, is a method that combines the capabilities of a pre-trained large language model with an external data source. This approach combines the generative power of LLMs like GPT-3 or GPT-4 with the precision of specialized data search mechanisms, resulting in a system that can offer nuanced responses. 

Why Use RAG to Improve LLMs? An Example

Let’s consider a scenario many businesses face today to demonstrate better RAG and how the technique works.

Imagine you are an electronics company executive selling devices like smartphones and laptops. You want to create a customer support chatbot for your company to answer user queries related to: 

• Product specifications
• Troubleshooting
• Warranty information and more

You’d like to use the capabilities of LLMs like GPT-3 or GPT-4 to power your chatbot.

Addressing Specificity Challenges in Large Language Models for Better Customer Support

Large language models have the following limitations, leading to an inefficient customer experience:

Lack of specific information

Language models are limited to providing generic answers based on their training data. If users were to ask questions specific to the software you sell, or if they have queries on how to perform in-depth troubleshooting, a traditional LLM may not be able to provide accurate answers.

This is because they haven’t been trained on data specific to your organization. Furthermore, the training data of these models have a cutoff date, limiting their ability to provide up-to-date responses.

Hallucinations

LLMs can hallucinate, which means that they tend to generate false responses based on imagined facts confidently. These algorithms can also provide off-topic reactions if they don’t have an accurate answer to the user’s query, leading to a bad customer experience.

Generic responses

Language models often provide generic responses that aren’t tailored to specific contexts. This can be a major drawback in a customer support scenario since individual user preferences are usually required to facilitate a personalized customer experience.

RAG effectively bridges these gaps by providing you with a way to integrate the general knowledge base of LLMs with the ability to access specific information, such as the data in your product database and user manuals. This methodology allows for highly accurate and reliable responses tailored to your organization’s needs.

How Does RAG Work?

Now that you understand what RAG is, let’s look at the steps involved in setting up this framework:

1. Data collection

You must first gather all the data that is needed for your application. In the case of a customer support chatbot for an electronics company, this can include: 

• User manuals
• A product database
• A list of FAQs

2. Data chunking

Data chunking is the process of breaking your data down into smaller, more manageable pieces. For instance, if you have a lengthy 100-page user manual, you might break it down into different sections, each potentially answering different customer questions.

This way, each chunk of data is focused on a specific topic. When information is retrieved from the source dataset, it is more likely to directly apply to the user’s query since we avoid including irrelevant information from entire documents.

This also improves efficiency, as the system can quickly obtain the most relevant information instead of processing entire documents.

3. Document Embeddings

Now that the source data has been broken down into smaller parts, it needs to be converted into a vector representation. This involves transforming text data into embeddings, numeric representations that capture the semantic meaning behind text.

In simple words, document embeddings allow the system to understand user queries and match them with relevant information in the source dataset based on the meaning of the text instead of a simple word-to-word comparison. This method ensures appropriate responses align with the user’s query.

If you’d like to learn more about how text data is converted into vector representations, we recommend exploring our tutorial on text embeddings with the OpenAI API.

4. Handling User Queries

When a user query enters the system, it must also be converted into an embedding or vector representation. To ensure uniformity, the same model must be used for both the document and query embedding.

Once the query is converted into an embedding, the system compares the query embedding with the document embeddings. It identifies and retrieves chunks whose embeddings are most similar to the query embedding, using measures such as cosine similarity and Euclidean distance.

These chunks are considered the most relevant to the user’s query.

5. Generating Responses With An LLM

The retrieved text chunks and the initial user query are fed into a language model. The algorithm will use this information to respond coherently to the user’s questions through a chat interface.

Practical Applications of RAG

We now know that RAG allows LLMs to form coherent responses based on information outside their training data. A system like this has a variety of business use cases that will improve organizational efficiency and user experience. 

Apart from the customer chatbot example we saw earlier in the article, here are some practical applications of RAG:

Text Summarization

RAG can use content from external sources to produce accurate summaries, saving considerable time. Managers and high-level executives are busy people who don’t have the time to sift through extensive reports.With an RAG-powered application, they can quickly access the most critical findings from text data and make more efficient decisions without reading lengthy documents.

Personalized Recommendations

RAG systems can analyze customer data, such as past purchases and reviews, to generate product recommendations. This will increase the user’s overall experience and ultimately generate more revenue for the organization.

For example, RAG applications can recommend better movies on streaming platforms based on the user’s viewing history and ratings. They can also analyze written reviews on e-commerce platforms.

Since LLMs excel at understanding the semantics behind text data, RAG systems can provide users with personalized suggestions that are more nuanced than those of a traditional recommendation system.

Business intelligence

Organizations typically make business decisions by monitoring competitor behavior and analyzing market trends. This is done by meticulously analyzing data in business reports, financial statements, and market research documents.

With an RAG application, organizations no longer have to analyze and identify trends in these documents manually. Instead, an LLM can be employed to derive meaningful insight and efficiently improve the market research process.

Challenges and Best Practices of Implementing RAG Systems

While RAG applications allow us to bridge the gap between information retrieval and natural language processing, their implementation poses a few unique challenges. In this section, we will look into the complexities faced when building RAG applications and discuss how they can be mitigated.

Integration Complexity

It can be difficult to integrate a retrieval system with an LLM. This complexity increases when multiple sources of external data in varying formats are present. Data fed into an RAG system must be consistent, and the embeddings generated need to be uniform across all data sources.

To overcome this challenge, separate modules can be designed to handle different data sources independently. The data within each module can then be preprocessed for uniformity, and a standardized model can be used to ensure that the embeddings have a consistent format.

Scalability

As the amount of data increases, maintaining the efficiency of the RAG system becomes more challenging. Many complex operations need to be performed, such as generating embeddings, comparing the meaning between different pieces of text, and retrieving data in real time.These tasks are computationally intensive and can slow down the system as the size of the source data increases.

To address this challenge, you can distribute computational load across different servers and invest in robust hardware infrastructure. Caching frequently asked queries might also improve response time.

Enhancing RAG Scalability with Vector Databases

Implementing vector databases can also mitigate the scalability challenge in RAG systems. These databases allow you to handle embeddings easily and quickly retrieve vectors that are most closely aligned with each query.

Data quality

The effectiveness of an RAG system depends heavily on the quality of data being fed into it. If the source content accessed by the application is good, the responses generated will be accurate.Organizations must invest in a diligent content curation and fine-tuning process. It is necessary to refine data sources to enhance their quality. For commercial applications, involving a subject matter expert to review and fill in any information gaps before using the dataset in an RAG system can be beneficial.

Related Reading

• LLM Security Risks
• What is an LLM Agent
• AI in Retail
• LLM Deployment
• How to Run LLM Locally
• How to Use LLM
• LLM Model Comparison
• AI-Powered Personalization
• How to Train Your Own LLM

What is LLM Fine-Tuning?

Fine-tuning takes a pre-trained model and further trains it on a domain-specific dataset. Most LLM models today have an excellent global performance but fail in specific task-oriented problems. The fine-tuning process offers considerable advantages, including lowered computation expenses and the ability to leverage cutting-edge models without building one from the ground up. Transformers grant access to an extensive collection of pre-trained models suited for various tasks. 

When and How to Fine-Tune Language Models for Specific Use Cases

Fine-tuning these models is crucial for improving the model's ability to perform specific tasks, such as: 

• Sentiment analysis
• Question answering
• Document summarization, with higher accuracy

Fine-tuning improves the model’s performance for specific tasks, making it more effective and versatile in real-world applications. This process is essential for tailoring an existing model to a particular task or domain. Whether to engage in fine-tuning hinges on your goals, which typically vary based on the specific domain or task. 

The Different Types of Fine-tuning

Fine-tuning can be approached in several ways, depending mainly on its main focus and specific goals. 

Supervised Fine-Tuning 

The most straightforward and common fine-tuning approach. The model is further trained on a labeled dataset specific to the target task to perform, such as text classification or named entity recognition. For instance, we would train our model on a dataset containing text samples labeled with their corresponding sentiment for sentiment analysis.

Few-Shot Learning

In some cases, collecting a large labeled dataset is impractical. Few-shot learning addresses this by providing a few examples (or shots) of the required task at the beginning of the input prompts. This helps the model have a better context of the task without an extensive fine-tuning process.

Transfer Learning 

Even though all fine-tuning techniques are a form of transfer learning, this category explicitly allows a model to perform a task differently from the initially trained one. The main idea is to leverage the knowledge the model has gained from a large, general dataset and apply it to a more specific or related task.

Domain-Specific Fine-Tuning 

This type of fine-tuning tries to adapt the model to understand and generate text specific to a particular domain or industry. The model is fine-tuned on a dataset of text from the target domain to improve its context and knowledge of domain-specific tasks. For instance, to generate a chatbot for a medical app, the model would be trained with medical records to adapt its language understanding capabilities to the health field.

A Step-by-Step Guide to Fine-tuning a LLM

We already know that Fine-tuning is taking a pre-trained model and updating its parameters by training on a dataset specific to your task. So, let’s exemplify this concept by fine-tuning an accurate model. Imagine we are working with GPT-2, but we detect it is pretty bad at inferring the sentiments of tweets. 

One natural question that comes to mind is: Can we do something to improve its performance? We can take advantage of fine-tuning by training our pre-trained GPT-2 model from the Hugging Face model with a dataset containing tweets and their corresponding sentiments so the performance improves. 

Step-by-Step Guide to Fine-Tuning GPT Models for Sequence Classification

Here's a basic example of fine-tuning a model for sequence classification: 

1. Choose a Pre-Trained Model and a Dataset.

• We must always have a pre-trained model in mind to fine-tune a model. 
• In our case, we will perform some simple fine-tuning using GPT-2. 
• Screenshot of Hugging Face Datasets Hub. 
• Selecting OpenAI’s GPT2 model. 

Always remember to choose a model architecture suitable for your task.

2. Load the Data To Use 

Now that we have our model, we need some good-quality data to work with, and this is precisely where the datasets library kicks in. In my case, I will use the Hugging Face datasets library to import a dataset containing tweets segmented by their sentiment (Positive, Neutral or Negative).

from datasets import load_dataset
import pandas as pd

# Load the dataset
dataset = load_dataset("mteb/tweet_sentiment_extraction")

# Convert the training dataset to a Pandas DataFrame
df = pd.DataFrame(dataset['train'])

3. Tokenizer 

Now that we have our dataset, we need a tokenizer to prepare it for parsing by our model. As LLMs work with tokens, we require a tokenizer to process the dataset. To process your dataset in one step, use the Dataset map method to apply a preprocessing function over the entire dataset. 

This is why the second step is to load a pre-trained Tokenizer and tokenize our dataset so it can be used for fine-tuning.

from transformers import GPT2Tokenizer
from datasets import load_dataset

# Load the dataset to train our model
dataset = load_dataset("mteb/tweet_sentiment_extraction")

# Load the GPT-2 tokenizer and set the padding token to the EOS token
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token

# Define a function to tokenize the examples
def tokenize_function(examples):
    return tokenizer(examples["text"], padding="max_length", truncation=True)

# Apply the tokenization function to the dataset
tokenized_datasets = dataset.map(tokenize_function, batched=True)

BONUS: To improve our processing requirements, we can create a smaller subset of the entire dataset to fine-tune our model. The training set will be used to fine-tune our model, while the testing set will be used to evaluate it.

# Create smaller subsets for training and evaluation
small_train_dataset = (
    tokenized_datasets["train"]
    .shuffle(seed=42)
    .select(range(1000))
)

small_eval_dataset = (
    tokenized_datasets["test"]
    .shuffle(seed=42)
    .select(range(1000))
)

4. Initialize our Base Model 

Start by loading your model and specify the number of expected labels. From the Tweet’s sentiment dataset card, you know there are three labels:

from transformers import GPT2ForSequenceClassification

# Load a pre-trained GPT-2 model for sequence classification with 3 labels
model = GPT2ForSequenceClassification.from_pretrained("gpt2", num_labels=3)

5. Evaluate Method 

Transformers provides a Trainer class optimized for training. This method needs to include evaluating the model, so before starting our training, we must pass the Trainer a function to evaluate our model performance.

import evaluate
import numpy as np

# Load the evaluation metric
metric = evaluate.load("accuracy")

# Define a function to compute metrics
def compute_metrics(eval_pred):
    logits, labels = eval_pred
    predictions = np.argmax(logits, axis=-1)
    return metric.compute(predictions=predictions, references=labels)

6. Fine-Tune Using the Trainer Method 

Our final step is to set up the training arguments and start the training process. The Transformers library contains the Trainer class, which supports various training options and features such as logging, gradient accumulation, and mixed precision. We first define the training arguments together with the evaluation strategy. Once everything is determined, we can train the model using the train() command. 

from transformers import TrainingArguments, Trainer

# Define training arguments
training_args = TrainingArguments(
    output_dir="test_trainer",
    # evaluation_strategy="epoch",  # Uncomment if evaluation per epoch is needed
    per_device_train_batch_size=1,  # Reduce batch size here
    per_device_eval_batch_size=1,  # Optionally, reduce for evaluation as well
    gradient_accumulation_steps=4
)

# Create Trainer instance
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=small_train_dataset,
    eval_dataset=small_eval_dataset,
    compute_metrics=compute_metrics
)

# Start training
trainer.train()

After training, evaluate the model's performance on a validation or test set. Again, the trainer class already contains an evaluate method that handles this.

pythonimport evaluate trainer.evaluate()`

These are the most basic steps to fine-tune any LLM. Remember that fine-tuning an LLM is highly computationally demanding, and your local computer might not have enough power to do so. 

Related Reading

• How to Fine Tune LLM
• How to Build Your Own LLM
• LLM Function Calling
• LLM Prompting
• What LLM Does Copilot Use
• LLM Evaluation Metrics
• LLM Use Cases
• LLM Sentiment Analysis
• LLM Evaluation Framework
• LLM Benchmarks
• Best LLM for Coding

A Comprehensive Rag vs LLM Tutorial with Practical Examples

Retrieval-augmented generation (RAG) combines standard language model capabilities with retrieval systems to enhance the quality of responses. With RAG, the language model works alongside a search engine to pull relevant information in real-time as it processes a query. It searches through a database or collection of documents to find information that adds context and helps the model craft its responses. RAG consists of four main components: 

1. Embedding model: When a user submits a question, RAG processes the query using vector embeddings of numerical data representations.
2. Retriever: The retriever searches these embeddings for the most relevant documents from the vector database. 
3. Reranker (optional): The reranker then assesses these documents to score their relevance to the query, ensuring the information aligns closely with the user’s needs. 
4. Language model: The language model takes the retrieved (and possibly reranked) documents, combines them with the query, and generates a precise answer. 

What are the Pros of Using RAG?

RAG can significantly improve the performance of language models, especially when accuracy is essential. 

• Up-to-date and relevant answers: Unlike standard models that might give outdated or irrelevant responses, RAG uses the latest information from various sources. 
• Less hallucinations: By regularly updating its database, RAG helps prevent the model from giving incorrect answers based on old or incomplete data. 
• Sources: RAG shows where its information comes from, which helps build trust and allows users to explore topics further. 
• Low maintenance: Once set up, RAG updates itself with new data, reducing the workload for developers. 
• Innovative Features: RAG can enable new product functions that improve user experience and engagement. 

What are the Cons of Using RAG?

RAG does require some resources and has a complicated interaction between systems. 

• Needs preprocessed data: RAG requires a large database of pre-processed data, which can be an extensive resource commitment. 
• Complicated interaction between systems: Setting up and maintaining these databases involves complex interactions, which can lead to additional latency between systems. 

When Should You Use RAG?

RAG is best for retrieval tasks, especially when you need up-to-date and precise information. Particularly, you might need RAG for: 

Better detail and accuracy

RAG shines when you need detailed and correct answers. It looks up relevant information while generating responses, ensuring the answers are smart and specific. This is especially useful in fields like medical research or legal document review, where precision is crucial. 

Dealing with complex questions

If you’re dealing with tough questions that require extensive knowledge or checking different facts, RAG can handle them by searching through lots of data to find the right answers. This capability is great for applications like language translation or educational tools, where understanding context is key. 

Keeping information consistent

In areas like law or healthcare, where it’s important to keep information consistent, RAG helps by using trusted documents. This keeps the answers reliable and accurate, which is essential for chatbots in customer service, where maintaining a consistent brand voice and accurate Information is critical. 

Up-to-date responses

If you need the latest info in your answers, RAG is useful because it constantly updates its responses based on the newest data it finds. This feature is particularly beneficial in fields that require staying current with the latest developments, like medical research. 

Tailored answers

You can set up RAG to look up information from specific places, making it perfect for fields that need accurate and relevant answers. This ensures the model gives responses that are correct and useful for your specific situation, such as in educational tools where personalized learning experiences are key. 

What Is Fine-tuning?

LLM fine-tuning takes a pre-trained language model and further trains it on a smaller, specialized dataset. This method is designed to adapt the model's general capabilities to specific tasks or industries by adjusting its parameters to reflect the nuances of the target domain. 

How Is Fine Tuning Performed?

Fine-tuning requires setting up a training pipeline and following standard machine learning practices. First, you select a pre-trained language model. Next, you gather a task-specific dataset to train the model on. You run the fine-tuning process and evaluate the model’s performance. 

What Are the Pros of Fine Tuning?

Fine-tuning has several advantages: 

• Customization: It allows for high levels of customization, making the model more relevant to specific tasks. 
• Less token usage: Your context window won’t be filled up with huge prompts. 
• Improved performance on specific tasks: Targets the peculiarities of a dataset, enhancing the model’s performance in specific applications. 

What are the Cons of Fine Tuning?

Fine-tuning does have some drawbacks: 

• Resource intensive: It can be expensive and timely because it needs a lot of computing power and data. 
• Overfitting: The model might learn the training data too well and not perform well on new, unseen data. 
• Data dependency: The results heavily depend on the quality and relevance of the data used for training. 
• Maintenance: You need to update and monitor the model regularly to ensure it remains effective as data and needs change. 

When Should You Use Fine Tuning?

Fine-tuning is a must if you need to align the model with your business-specific needs, tone of voice, writing style, and a lot more: 

Domain adaption

Thanks to their broad training, LLMs are knowledgeable, but they might need to learn your sector's unique language or details. Fine-tuning helps the model better understand and generate content that fits your business requirements. 

Precision and accuracy

Accuracy is crucial in business; even minor errors can have significant consequences. Training the model with your business-specific data can significantly enhance its precision, ensuring that the outputs closely align with your expectations. 

Customized user interactions

For roles involving direct customer interaction, such as chatbots, fine-tuning allows you to adjust the model to reflect your brand’s voice and guidelines, ensuring a consistent and engaging customer experience. 

Control over data

General models might use publicly accessible data, posing a risk if sensitive information is involved. Fine-tuning allows you to limit the data the model uses, enhancing content security and preventing accidental data leaks. 

Specialized situations

Each business faces unique, critical situations that a broadly trained model may need to address better. Fine-tuning ensures the model is well-equipped to handle these niche scenarios more effectively. 

RAG vs. Fine Tuning: Which One to Choose?

As we’ve learned, RAG and fine-tuning are ways to improve your LLM at certain things. They have the same big goal but work differently. Here are some RAG vs. fine-tuning feature comparisons to determine your project's best fit. 

Knowledge Updates

RAG is like your always-updated AI assistant, integrating the latest information without needing frequent retraining. This makes it ideal for industries where staying current is crucial. Fine-tuning, on the other hand, is more like a specialist trained for a specific job. It excels within its domain but requires periodic updates and retraining to keep up with new information. 

Data Integration

RAG is a data chameleon adept at seamlessly blending a wide range of external information into its responses. It handles both structured and unstructured data with ease. Fine-tuning prefers its data to be well-prepared and polished, relying on high-quality datasets to function effectively. 

Reducing Hallucinations

RAG’s answers are rooted in reality, thanks to its direct data fetching, which minimizes made-up or incorrect information. While generally reliable, fine-tuning can occasionally produce incorrect or imaginative answers, especially with complex or unusual queries not covered in its training data. 

Customization Capabilities

RAG sticks to the script but may not be fully customized for model behavior or writing style. In contrast, fine-tuning can be tailored to the finest detail, including writing style and domain-specific terms, allowing it to meet the exact needs of a given scenario. 

Interpretability Factor

With RAG, you can easily trace how it went from question to answer, making it an open book regarding interpretability. Fine-tuning, though capable of impressive results, can sometimes be like a brilliant magician amazing, but only occasionally clear on how the results were achieved. 

Latency

RAG involves heavy data retrieval, which makes it thorough but sometimes slow, leading to higher latency. Fine-tuning is quicker, as it doesn’t need to retrieve data and can deliver answers almost instantly, although it requires significant setup initially. 

Ethical and Privacy Considerations

RAG’s extensive data reach must be handled carefully to protect privacy. Fine-tuning, and focusing on specific datasets also has challenges ensuring that the data it learns from is used responsibly. 

Scalability

RAG easily scales to handle large volumes of data from multiple sources. Fine-tuning requires careful data management and model training, which can be more resource-intensive when scaling to larger datasets. 

Hybrid Approaches: RAG + Fine Tuning

In some cases, combining RAG and fine-tuning can yield the best results. 

• Retrieval-Augmented Fine-Tuning: For example, using RAG to retrieve relevant information and then fine-tuning the model on that data can lead to more accurate and tailored outputs. 
• Fine-Tuning An Rag Component: If you want to improve your RAG system, you can find its defective component and fine-tune it separately. 

Final Thoughts

When choosing between RAG and fine-tuning for your project, think about your specific needs. RAG is a good fit if you need your model to stay up-to-date and handle a wide range of data. It's especially helpful in fast-changing environments where accuracy and timely information are crucial.

Fine-tuning works best for tasks that require specialized, precise responses. It's ideal when your model needs to follow specific guidelines or operate within stable, consistent data. Your choice depends on whether you:

• Prioritize adaptability
• Broad knowledge
• Precision in a specialized area

Mixing both approaches can be the best way to balance staying current with accuracy. To make the best decision, consider your project's unique demands, the resources you have, and your long-term goals.

Related Reading

• LLM Quantization
• LLM Distillation
• LLM vs SLM
• Best LLM for Data Analysis
• Foundation Model vs LLM
• ML vs LLM
• LLM vs Generative AI
• LLM vs NLP

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