What is Gen AI? What is RAG system AI?

This article will seek to provide answers on what Gen AI is and what a RAG System AI is. Atlas Fuse and the AI Tooling it contains is a RAG Based Generative AI, so we hope this information will be useful to you as you position AI within your business and uncover, confirm, build and deliver scenarios where you can create value and save time through AI.

We will provide some common high level scenarios and use cases you could look to use AI for, but also point out the difference between Search and AI, as they're not the same thing and should be treated as distinctly different toolsets, and also some scenarios where AI will not provide value.

• What is Gen AI?
• What is RAG system AI?
  • Why is RAG AI useful?
  • How RAG Works:
  • Benefits of RAG:
• Use Cases:
• Differences from Search
• Scenarios Gen AI should not be used for

What is Gen AI?

Generative AI, or Gen AI, refers to "a type of artificial intelligence that can create content, such as text, images, music, and more, based on the input it receives. Gen AI uses sophisticated Machine Learning and Large Language models (LLMs)."

Unlike traditional Search that retrieves and displays existing information, generative AI produces new content by understanding patterns in data and replicating those patterns to produce an output.

The output is generated based on the data the model has been trained on, but it cannot be guaranteed to be correct. It is important to understand that Gen AI does not actually know the meaning behind the input or output, it is simply generating data based on patterns and using probability to essentially "guess" the next word.

Gen AI can have difficulty understanding information outside the context in which it was trained, so it is important to provide the model with relevant data for each use case. If the model does not contain relevant data it is very unlikely that Gen AI will provide a useful response. 

What is RAG system AI?

Retrieval-Augmented Generation (RAG) is "an AI framework that enhances LLMs by integrating them with internal or external knowledge bases, allowing them to access and incorporate information beyond their training data, resulting in more accurate and contextually relevant responses."

Why is RAG AI useful?

Combines Strengths:

RAG combines the generative capabilities of LLMs with the information retrieval strengths of traditional search systems.

Augments LLMs:

It augments LLMs by enabling them to access and incorporate information from specific knowledge sources, such as documents, lists, pages, or other supported content types, before generating a response.

Improved Accuracy and Relevance:

By accessing and using authoritative knowledge, RAG helps LLMs provide more accurate, up-to-date, and contextually relevant answers.

Cost-Effective:

RAG allows LLMs to produce highly specific outputs without extensive fine-tuning or retraining, offering some of the benefits of a custom LLM at a lower cost.

Addresses LLM Limitations:

RAG helps address the limitations of LLMs, such as their reliance on static training data, which can lead to outdated or generic responses.

How RAG Works:

1. Retrieval:

A user query is fed into a search or retrieval system, which identifies relevant information from an external knowledge base.

2. Augmentation:

The retrieved information is then passed to the LLM, along with the user's query, to augment the model's knowledge.

3. Generation:

The LLM uses both its own knowledge and the retrieved information to generate a response that is more accurate and contextually relevant.

Benefits of RAG:

Improved Accuracy:

RAG helps LLMs provide more accurate and factually correct answers by grounding their responses in external knowledge.

Enhanced Context:

RAG enables LLMs to generate responses that are more relevant to the specific context of the user's query.

Reduced Hallucinations:

By grounding LLMs in knowledge, RAG can help reduce the occurrence of "hallucinations," where LLMs generate plausible but incorrect information.

Access to Specific Data:

RAG allows LLMs to access and utilize specific data sources, such as internal company data or specialized datasets, to provide more tailored responses.

Cost-Effective:

RAG can be a cost-effective way to improve the performance of LLMs without the need for extensive retraining or fine-tuning.

Use Cases:

Q&A or Support Chatbots:

RAG AI can be used to create chatbots that can access and utilize an organization's knowledge base to provide more accurate and relevant answers to customer inquiries.

Question Answering Systems:

RAG AI can be used to build question-answering systems that can access and utilize knowledge sources to provide more accurate and comprehensive answers.

Content Generation:

RAG AI can be used to generate content that is more accurate, contextually relevant, and based on authoritative sources. This might be marketing content, internal news letters, training and onboarding material, or even proposal or contract drafting

Research and Information Retrieval:

RAG AI can be used to help researchers and information seekers find and access relevant information from a variety of sources.

Comparisons & Summarizations

RAG AI is very good at reducing information down to summaries because it simply needs to provide the same information using less words. It can also be great at summarizing differences and key points between different documents or paragraphs.

Differences from Search

• Output Type: Search engines provide access to existing information, while generative AI creates new content.
• Interactivity: Generative AI can engage in multi-step conversations and provide personalized responses as well as taking existing responses and refining them as instructed. Search engines return static results and only allow refinement by specific supported metadata.
• Context Understanding: Gen AI can generate contextually relevant information based on your query, whereas search engines rely purely on keyword matching and returning results including the same keywords.

Scenarios Gen AI should not be used for

In general, we do not recommend using AI for scenarios where there is no return on investment. Ideally AI should be used to reduce the time humans need to spend on tasks that generate revenue for your business.

Therefore the following types of task are not recommend for using Gen AI:

• Simple document retrieval  - Finding a specific policy document or employee handbook. Atlas search is designed to quickly locate existing documents with supplementary tagging to make this process efficient. The AI will not know if this is in fact the correct document, the user will still have to check.
• Simple fact retrieval - looking up a capital of a country or a date of an event, for example. These tasks are straightforward and can be quickly handled by a search engine.

• Navigational Queries - such as finding the location of a specific department's intranet page. Atlas search and navigation elements (IA) excels at directing users to the right internal location.

• Highly specialized knowledge - detailed medical advice, legal interpretations, technical assessments - these require expert knowledge and should be handled by professionals
• Real-time data from the real world - such as stock prices or sports events. There are more accurate ways for finding up-to-the-minute 'live' information
• Personalized recommendations based on limited data - AI needs substantial data to make accurate recommendations, and even then should be taken as advice not as a trusted source of truth when decision making
• Tasks requiring human judgement - ethical decisions or nuanced customer service interactions. These often require empathy and complex decision making that AI cannot fully replicate.