In an era where AI technology is changing our world. An exciting new innovation is emerging: "Agentic Retrieval", which will make AI a smarter and more understanding assistant than ever before.
AI today: capabilities and limitations
Nowadays, we are familiar with AI that can generate text. You can answer questions, or even code.
But these AIs often work on the basis of pre-trained data. Sometimes, the information may not be up to date or meet our current needs.
An obvious example is ChatGPT, which can answer a wide variety of questions and is amazing. For example, if you ask about the latest election results or news that just happened yesterday, ChatGPT may not be able to provide accurate information because the data available is limited by the data cutoff date.
This is a major limitation that prevents traditional AI from fully meeting the needs of real-world applications.
What is Agentic Retrieval?
Agentic Retrieval is a technology that builds on Retrieval-Augmented Generation (RAG) by increasing the ability to automatically search and process data.
This makes AI more accessible and relevant to the needs of users.
(An Overview of Agentic RAG from Research)
Difference Between RAG and Agentic RAG
- RAG works by pulling data from external sources and using it to generate answers.
- Agentic RAG goes beyond that, using AI agents who can make their own decisions to find and process data. This makes it possible to obtain more accurate and context-appropriate results.
The Technology Behind Agentic Retrieval
Agentic Retrieval consists of several technologies that work together:
- AI Agents: It is like a smart assistant that can think, plan, and make decisions on its own.
- Large Language Models (LLMs): Large language models that can understand and generate human language
- Information Search System: Use advanced techniques to find relevant information from different sources.
- Parallel Processing: This makes it possible to quickly handle multiple tasks at once.
An example of how these technologies work together is when a user asks a question about the weather today, the AI Agent analyzes the question and decides what information is needed.
It then instructs the search system to retrieve the latest weather data from the Meteorological Department's API.
The resulting data is then sent to the LLM to process and generate an easy-to-understand answer for the user. The whole process takes just a few seconds. With parallel processing, all processes work simultaneously efficiently.
Examples of Real-Life Agentic Retrieval Applications
- Medical Field: AI can analyze treatment history, medical results, and recent health information to propose the most appropriate treatment approach for each patient.
- Customer Service: AI systems can understand customer problems. Find relevant information and provide on-the-spot recommendations quickly. Without waiting for the authorities.
- Financial aspects: AI can analyze financial market data in real-time. Assess risks and provide investment advice that is appropriate to the current situation.
- Education: The teaching and learning system adapts to the needs of learners, with AI finding and presenting content that is suitable for each person's knowledge level and learning style.
Challenges and the Future of Agentic Retrievals
Although Agentic Retrieval has great potential, there are still challenges to overcome. Such as:
- Efficient handling of big data
- Data Security and Privacy
- Developing AI to understand diverse contexts and cultures
however Researchers and developers are working hard to solve these problems, and it is expected that in the near future, Agentic Retrieval will become a technology that plays an important role in our daily lives.
Interesting development trends in the near future include:
- Developing more complex multi-agent systems: Researchers are developing systems that use multiple AI agents to work together in complex ways to handle highly complex and multi-disciplinary tasks.
- Integration of multiple data formats: Agentic RAG is evolving to handle multiple data formats, including text, images, audio, and video, to better analyze and respond to complex situations.
- Ethical and transparent AI development: Research and development is carried out to create an ethical framework for Agentic RAG with a focus on fair, transparent, and auditable decision-making.
With these developments, we may see Agentic Retrieval that can work smarter, understand context, and respond to human needs more accurately in the near future.
conclusion
Agentic retrieval is an important step in the development of AI to be smarter and more useful. By combining language comprehension, data search, and automated decision-making.
This technology will allow us to solve complex problems more efficiently and could be the beginning of a new era of human-AI interaction.
Agentic Retrieval is believed to be a technology that will transform the way we work and live, enabling AI to become an assistant that understands us deeply and accurately meets our needs.
Developments in this area will lead to new innovations that we may not have imagined right now, and will be key to solving global challenges in the future.
Technical terms you should know
- Agentic RAG: An AI system that uses intelligent agents to automatically search and process data.
- Multi-Agent Collaboration: Collaboration of multiple AI agents to solve complex problems
- Retrieval-Augmented Generation (RAG): A technique used to optimize AI by extracting data from external sources to generate answers.
- Large Language Models (LLMs): Large-scale AI models that can understand and construct human language in complex ways.
- Autonomous AI Agents: An AI system that can think, make decisions, and work on its own without human control at all times.
References
Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
Chat with research papers