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2024

Comparing Prompt Engineering, RAG, and Fine-Tuning for LLMs

Large Language Models (LLMs) like GPT-4 can do many amazing things. But sometimes, we want them to work better for specific tasks or topics. Three common approaches to enhance LLM capabilities are Prompt Engineering, Retrieval Augmented Generation (RAG), and Fine-tuning. This guide will help you understand when to use each method.

What Is Prompt Engineering?

Prompt engineering is crafting effective prompts to get the desired output from an LLM without modifying the model itself. This involves using methods such as chain of thoughts, multi-shot prompting etc.

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Chain Of Thought Example

Pros of Prompt Engineering

  • No Infrastructure Needed: Can be implemented immediately without additional systems
  • Cost-Effective: Doesn't require additional training or data processing
  • Flexible: Can quickly adjust and iterate on prompts for different use cases

Cons of Prompt Engineering

  • Token Limitations: Long prompts consume more tokens, increasing costs
  • Inconsistent Results: May not always produce consistent outputs

What Is Fine-Tuning?

Fine-tuning means taking a pre-trained LLM and training it more on special input and output pair examples for a specific task. It’s like teaching the model new tricks by showing it input and output pair examples.

Pros of Fine-Tuning

  • Character Roleplaying: Fine-tuning generally are better to use it for copying a writing style, Example: Customer Support Agent, Financial Report.
  • Faster Answers: Since it doesn't need to retrieve information first, it can generate response quicker.

Cons of Fine-Tuning

  • Needs Lots of Data: You need enough high-quality data to train the model well. Fine-tuning are also NOT reliable to generate accurate industry specific data.
  • Time and Resources: Fine-tuning can take higher cost.
  • Static Knowledge: Can't easily update knowledge without retraining

What Is Retrieval Augmented Generation (RAG)?

RAG is a method where the LLM use information from external sources to generating answers. The LLM model has real-time access to external data source to find the most relevant information before generating an answer.

Pros of RAG

  • Access to New Information: The model can provide answers using the most recent data.
  • Variety Data Source: Can handle a different type of data source (PDF, CSV , Word) without extra training.
  • Saves on Training: You don’t need to fine-tune the model for every new topic.

Cons of RAG

  • Technicality: Need higher technicality to ensure retrieved data is accurate
  • Slower Responses: Fetching information then pass into LLM can take slightly longer time
  • Depends on Data Quality: If the retrieved information are irrelevant/incomplete, the generated answer will be wrong.

Fine-Tuning vs. RAG: Which One to Choose?

When to Use Prompt Engineering?

  • Quick Implementations: When you need a solution immediately
  • Simple Use Cases: For straightforward tasks that don't require external knowledge
  • Testing and Prototyping: To validate ideas before investing in RAG or fine-tuning

When Should You Use Fine-Tuning?

  • Roleplaying: You want the AI to write in a specific way (like a customer service agent)
  • Fast Response: You need fast responses
  • Enough Data: You have enough data to fine-tune the model
  • Enough Time and Money: You have enough time and money for training

When Should You Use RAG?

  • Up-to-Date Information: If you need the latest news or data that’s not in the model’s training.
  • Broad Topics: When the model needs to handle questions about many different subjects.
  • Limited Training Data: If you don’t have much data to fine-tune the model.

Frequently Asked Questions

Can you use multiple approaches together?

Yes, it's common to combine these approaches. For example: - Start with prompt engineering for quick implementation - Add RAG for knowledge-intensive tasks - Use fine-tuning later to improve specific aspects of the system

Is Prompt Engineering important?

Yes, it is important. Prompt engineering provide the base for giving instructions to the LLM. It is also the most cost-effective way to get started with LLM.

Want to learn more?

What is RAG (Retrieval Augmented Generation)?

Do you want to create an AI chatbot? But How?

If you’re thinking about fine-tuning a LLM with your personal knowledge, you might want to think again.

The Problems with Fine-Tuning

Fine-tuning means training an existing model with new, specific data. It might sound like a good idea, but there are some big issues:

  • Not Enough Data: Most people don’t have lots of high-quality data to teach the model properly.
  • Hallucination: Fine-tuned models can give wrong or strange answers, even after training.
  • Costing: Even with enough data, fine-tuning is time-consuming and expensive.

Meet RAG: A better way

Here's what you need instead, RAG a.k.a Retrieval Augmented Generation.

Instead of training the model, RAG helps the chatbot give better answers by retrieving the correct information and then passing it to the LLM. This greatly reduces hallucination as the LLM is only using relevant information to answer the question.

Think of RAG as an open book exam. LLM can look up information from the book in real time to answer question.

Fine-tuning a model however is like an closed book exam. LLM has to rely on its training data to answer questions.

RAG vs Fine-Tuning

Note

Click on the image to zoom in.

Advantages of RAG

  • Up to Date: Chatbot can always fetch the latest information through RAG. You wont need to retrain the model every single time your business information changes.
  • Less Hallucination: Because LLM is only using relevant information to answer the question, it is less likely to hallucinate.
  • Integration: You can implement AI with your existing systems. (eg. Teams, Slack, Calendar, etc.)

How Big Companies are Using RAG

  • Grab: Grab uses RAG in their tool called Mystique to create personalized messages for each user. RAG helps Mystique find and use the right information from Grab’s data so the messages match each person’s preferences and past activities.
  • Shortwave: Shortwave email assistant uses RAG to select the most relevant data source then fetch relevant information to compose the best response.
  • Pinterest: Pinterest built a Text-to-SQL to generate SQL Queries for their database.

Note

RAG is not a replacement for fine-tuning. It is a supplement to fine-tuning.

Want to Learn More About RAG?