Q 716. Compare RAG with fine-tuning for an LLM-powered Agent. When will you use RAG? When will you use fine-tuning? When would you like to use a combination? Note for website visitors - This platform hosts two weekly questions, one on Tuesday and the other on Friday. All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/. The question will be open until Tuesday or Friday at 5 PM Indian Standard Time, depending on the launch day. Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be approved. If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term. Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error prone as our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/ We also use an AI content detector at https://quillbot.com/ai-content-detector. Only answers with less than 45-50% AI-generated content will be approved.

Retrieval-Augmented Generation (RAG) and fine-tuning for LLM-Powered Agent (Large Language Models) are both types of AI language generation methods of Natural Language Processing (NLP) with some fundamental difference in response generation. Let's understand: In RAG, the response generation is based on external source of knowledge that helps with real-time information update & the same information is used to augment the model's response. Basically, instead of relying on internal knowledge used for model training, RAG has kind of "Open-Book" approach where any additional or real-time information is looked from external sources. Whereas in Fine-tuning for an LLM-Powered Agent, there is pre-existing internal data source which is fine-tuned through continuous improvement on a specific set of data to specialize it for specific task or domain. Say for example, we are adding specific books in internal data sources for generation of more advanced & niche responses which industry or domain specific. Application for RAG: Dynamic & real-time knowledge requirements (e.g. stock price, latest news, latest research etc.) Model Size Reduction (as no need for huge internal data source required) Cost & Efficiency (requirement of low or no internal data eliminates requirement of high energy consuming servers for storing internal database) Application for Fine-Tuning with LLM Powered Agent: Nuances & Specialized Tasks (e.g. application with special domain language, knowledge and jargons like medical, legal etc.) Consistent Tone and Style (nuanced in line with ask in the prompt) Hybrid Use Case (RAG & Fine-Tuning with LLM Powered-Agent) In cases where we need the response that is specialized in language and also dynamic with respect to real-time or latest information update, we need both internal and external sources for response generation. In this scenario, we need to explore the Hybrid model. Examples: Let's say we have created Financial Advisor Chatbot which uses finance related language and tax laws using (Fine-Tuning with LLM) as well as gives advice considering latest market dynamics (stocks, bonds, investment avenues etc. - RAG). In this case, it's best to use both internal and external sources to generate ideal response. To summarize, hybrid model is ideal scenario for specialized and real-time response generation.

RAG alongside fine-tuning are two distinct approaches with different capabilities that a large language model can acquire. RAG (Retrieval-Augmented Generation) What it is: RAG is a combination of language model and retrieval system. This means that it can get a reference from a database or a document in order to provide the most exact and actual information. When to use it: RAG works best when the model has access to the bulk of data that might be time-varying such as company policies and product details. It is a perfect way to do tasks where the reference or data is quickly changing. Fine-Tuning What it is: Fine-tuning is the process of making the language model better at specific tasks by training it on a certain dataset. The parameters are then set up to align the model to the new data. When to use it: The fine-tuning method is the best choice when you require high performance in the model when performing a specific task or set of tasks, for example, customer service for a specific product, and interpreting medical terminology. It helps in such cases when the data does not vary regularly, and you need high precision. Using Both Together When to combine: There are times when you may wish to switch between RAG and fine-tuning, which is also an option. For instance, you may fine-tune a model to fit your organization’s customer service to then exploit RAG for the new product information. Hence, the model is both knowledgeable and current.

Message added by Mayank Gupta, November 1, 20241 yr

Retrieval Augmented Generation (RAG) is a powerful hybrid model that merges the best of two worlds: retrieval-based systems and generative models. In RAG, the system first retrieves relevant information from a knowledge base, and then uses this data to generate more accurate and contextually relevant responses. This allows the system to provide fact-based, enriched answers that go beyond standard generative models, which rely only on their training data.

An application-oriented question on the topic along with responses can be seen below. The best answer was provided by Deep Dave on 1st Nov 2024.

Applause for all the respondents - Sachin Tanwar, Deep Dave.

Retrieval Augmented Generation (RAG)

Followers

October 30, 20241 yr

Q 716. Compare RAG with fine-tuning for an LLM-powered Agent. When will you use RAG? When will you use fine-tuning? When would you like to use a combination?

Note for website visitors -

This platform hosts two weekly questions, one on Tuesday and the other on Friday.
All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/.
To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/.
The question will be open until Tuesday or Friday at 5 PM Indian Standard Time, depending on the launch day.
Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be approved.
If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting.
All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term.
Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error prone as our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/
We also use an AI content detector at https://quillbot.com/ai-content-detector. Only answers with less than 45-50% AI-generated content will be approved.

Solved by Deep Dave

November 1, 20241 yr

Go to solution

October 31, 20241 yr

RAG alongside fine-tuning are two distinct approaches with different capabilities that a large language model can acquire.

RAG (Retrieval-Augmented Generation)

What it is: RAG is a combination of language model and retrieval system. This means that it can get a reference from a database or a document in order to provide the most exact and actual information.
When to use it: RAG works best when the model has access to the bulk of data that might be time-varying such as company policies and product details. It is a perfect way to do tasks where the reference or data is quickly changing.

Fine-Tuning

What it is: Fine-tuning is the process of making the language model better at specific tasks by training it on a certain dataset. The parameters are then set up to align the model to the new data.
When to use it: The fine-tuning method is the best choice when you require high performance in the model when performing a specific task or set of tasks, for example, customer service for a specific product, and interpreting medical terminology. It helps in such cases when the data does not vary regularly, and you need high precision.

Using Both Together

When to combine: There are times when you may wish to switch between RAG and fine-tuning, which is also an option. For instance, you may fine-tune a model to fit your organization’s customer service to then exploit RAG for the new product information. Hence, the model is both knowledgeable and current.

November 1, 20241 yr

Solution

Retrieval-Augmented Generation (RAG) and fine-tuning for LLM-Powered Agent (Large Language Models) are both types of AI language generation methods of Natural Language Processing (NLP) with some fundamental difference in response generation. Let's understand:

In RAG, the response generation is based on external source of knowledge that helps with real-time information update & the same information is used to augment the model's response. Basically, instead of relying on internal knowledge used for model training, RAG has kind of "Open-Book" approach where any additional or real-time information is looked from external sources.

Whereas in Fine-tuning for an LLM-Powered Agent, there is pre-existing internal data source which is fine-tuned through continuous improvement on a specific set of data to specialize it for specific task or domain. Say for example, we are adding specific books in internal data sources for generation of more advanced & niche responses which industry or domain specific.

Application for RAG:

Dynamic & real-time knowledge requirements (e.g. stock price, latest news, latest research etc.)
Model Size Reduction (as no need for huge internal data source required)
Cost & Efficiency (requirement of low or no internal data eliminates requirement of high energy consuming servers for storing internal database)

Application for Fine-Tuning with LLM Powered Agent:

Nuances & Specialized Tasks (e.g. application with special domain language, knowledge and jargons like medical, legal etc.)
Consistent Tone and Style (nuanced in line with ask in the prompt)

Hybrid Use Case (RAG & Fine-Tuning with LLM Powered-Agent)

In cases where we need the response that is specialized in language and also dynamic with respect to real-time or latest information update, we need both internal and external sources for response generation. In this scenario, we need to explore the Hybrid model.

Examples: Let's say we have created Financial Advisor Chatbot which uses finance related language and tax laws using (Fine-Tuning with LLM) as well as gives advice considering latest market dynamics (stocks, bonds, investment avenues etc. - RAG). In this case, it's best to use both internal and external sources to generate ideal response.

To summarize, hybrid model is ideal scenario for specialized and real-time response generation.