Lately, AI has been expanding its presence across the entire digital world. It is coming into the spotlight in some form or another. From searching for the most straightforward question, you might have to create videos, reach out to customer support, and the list goes on.
Well, aside from that, RAG and Agentic RAG are the two major players booming in the AI world. RAG (Retrieval Augmented Generation) provides you with super-quick and accurate answers by retrieving your data. While Agentic RAG goes above and beyond by using autonomous AI agents, learning from past information, solving complex problems, and more.
However, the debate goes on which one to choose. RAG vs. Agentic RAG is a race, and to better understand, we’ve curated this blog. Here, we will explore the terms RAG and Agentic RAG, including their features, benefits, differences, and other relevant aspects.
So, without any doubt, you can pick the one that’s best for your business tasks.
No more pulling it! Let’s begin!
What Is Agentic RAG?
Agentic AI is a type of AI that can perform tasks autonomously, meaning it can act independently of human intervention. It can take input, make decisions, take actions, and perform many other functions with goal-oriented behavior.
These AI agents are proactive; they learn and adapt to their environments, data, and other factors without requiring human intervention. Enter the world of AI that aligns well with scenarios that can navigate complex tasks effectively.
Key features of Agentic RAG:
- Can handle complex queries by adjusting responses.
- It utilizes autonomous AI agents that primarily operate independently of human intervention.
- Focused on achieving goals and taking the best possible path to reach them.
- A deep understanding of the context.
It improves on its own. Work on feedback loops that help them analyze the downsides and change accordingly.
What is RAG (Retrieval Augmented Generation)?
Retrieval-augmented generation is another AI technique that utilizes large language models (LLMs) in conjunction with external knowledge sources to enhance the accuracy of AI-generated responses.
Instead of relying entirely on training data, RAGs' AI models access data in real time through APIs and various connections to sources. It pulls data from multiple sources and adds contextual information to provide accurate responses. This approach works best in scenarios such as AI for customer support, where information retrieval is essential.
Key Features of Traditional RAG
- Ideal for static queries
- It can be implemented easily and scalable.
- The focus is on accuracy rather than complexity.
- One of the key strengths is control. Define the scope of knowledge; it doesn’t matter if it is manual, legal documents, etc.
- RAG is reactive; it responds to the data it retrieves.
Benefits of Agentic RAG in Certain Fields
1] Healthcare Diagnostics
In healthcare diagnostics, Agentic RAG can autonomously analyze patient data and medical history, supporting informed decision-making for accurate diagnosis and treatment. It further reduces human error and fosters proper disease diagnosis.
2] Supply Chain Management
In supply chain management, operations are constantly changing. Thus, Agentic RAG certainly helps in this case. It can automate logistics, change according to market and weather changes, and more. In short, the new AI form can adjust autonomously to ensure efficiency at all times.
Advantages of Traditional RAG
As discussed, RAG is intended for straightforward purposes, where the focus is on accuracy rather than complexity.
1] FAQ
Provide reliable and accurate answers to frequently asked questions, thereby reducing manual human work.
2] Customer Support
Indeed, customer support is the heart of any business. RAG delivers fast and contextual responses to queries. This helps support teams to handle high volumes of inquiries.
3] Knowledge Base Search
Allows users to retrieve information quickly from documentation, making it easy for them to access everything they need.
Key Differences Between RAG and Agentic RAG
Checkout the key differences in the RAG vs Agentic RAG rundown.
1] Accuracy
Traditional RAG does not verify or improve its results, so users must judge whether they are acceptable or not. They have no way of knowing if the retrieved information is accurate or suitable for the context. In contrast, AI agents can review and revamp their work to get the desired results on time.
2] Flexibility
In terms of flexibility, Agentic RAG extracts data from different external knowledge bases. Additionally, it allows the use of external tools. However, traditional RAG pipelines are typically connected to an LLM using a single external dataset.
3] Autonomy
The difference is simple: RAG pulls information based on user queries. It’s less proactive and cannot respond to requests if no action is initiated. Conversely, Agentic RAG works autonomously. This means that you don’t need to provide inputs constantly; Agentic can make decisions on its own, take actions, and more.
4] Personalization
RAG offers responses tailored to the information retrieved. However, it cannot personalize the interactions using past data. Alternatively, Agentic outperforms traditional RAG by customizing the reactions based on previous interactions.
5] Flexibility and Adaptability
RAG systems have low adaptability. They can pull the latest information; however, they don’t learn or improve on their own beyond training. On the other hand, Agentic RAG is highly flexible, as it learns from each decision and adjusts its strategies accordingly to enhance performance. This adaptability is ideal in conditions where frequent changes occur, such as in finance and supply chain management.
Challenges and Ethical Considerations for RAG vs Agentic RAG
Privacy Issues
As both AI systems rely on data, this raises concerns about data privacy and security. Thus, you must always prioritize security.
Unemployment
AI can impact the workforce vastly. Prepare employees to adapt to the trend of AI and utilize it effectively.
Hallucinations
AI can sometimes produce incorrect information, which is a matter of concern in fields such as healthcare and research. Therefore, accuracy must be taken into account.
High-Security Risks
Nonetheless, AI systems are prone to cyberattacks, particularly when they involve external links. You should incorporate proper security measures to prevent unexpected events.
Making a choice: RAG and Agentic RAG.
As we come to the end of this blog, let’s summarize in a nutshell which RAG system is right for your business.
- If you are looking for straightforward tasks, in short, retrieve information. It’s budget-friendly too.
- If performing complex tasks and dynamic reasoning is on your list, then Agentic RAG is your go-to option. Additionally, it is well-suited for large-scale applications in high-stakes industries.
Towards the Final Words!
Traditional RAG and Agentic RAG are the most prominent frameworks in the AI industry today. Moreover, both are unique in their own right and serve different purposes. Traditional RAG, as discussed above, is designed for accurate responses and is reactive, whereas Agentic RAG provides autonomous responses. I hope the differences mentioned above have helped you understand more! The future, indeed, lies at the top of the sky, holding endless possibilities ahead.
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F&Qs
Q1. What is the difference between RAG and agentic search?
Answer: The basic difference is that RAG focuses on extracting the right information from knowledge sources to generate accurate responses or answers. Alternatively, Agentic search empowers AI to refine queries and enhance performance by utilizing multiple tools to achieve better results.
Q2. What is agentic RAG for beginners?
Answer: Agentic RAG is a system that goes beyond traditional RAG. It uses AI agents to improve answers.
Also Read:
Retrieval-Augmented Generation (RAG) Security: Risks and Mitigation Strategies
