What is Agentic RAG?

Summary

Retrieval Augmented Generation (RAG) enhances language model responses by adding context from external data sources. The evolution to Agentic RAG introduces an intelligent agent that not only retrieves relevant information but also decides which data source to query and how to present the response. This system offers more tailored and accurate results by leveraging the LLM's decision-making capabilities. Agentic RAG has broad applications in fields like customer support, legal tech, and healthcare, enabling AI to understand context and deliver more relevant, adaptable responses in real-time.

Takeaways

• 😀 Retrieval Augmented Generation (RAG) enhances responses from large language models by incorporating relevant data from a vector database for more accurate and reliable answers.
• 😀 The RAG process involves querying a vector database, retrieving relevant data, and adding it as context to a prompt before sending it to the language model for response generation.
• 😀 Traditional RAG only uses the language model for generating responses based on provided context, but Agentic RAG introduces an additional layer where the LLM also makes decisions about the process.
• 😀 Agentic RAG uses the LLM as an agent to decide which vector database to query based on the context of the query, making the process more intelligent and adaptive.
• 😀 Agentic RAG can also determine the type of response needed (e.g., text, chart, or code snippet), depending on the query context and requirements.
• 😀 The agent in Agentic RAG does not make random decisions; it leverages the LLM’s language understanding to intelligently interpret queries and select the most relevant database for response generation.
• 😀 For more complex queries, like those asking for company-specific data or general industry knowledge, Agentic RAG intelligently routes the query to the appropriate internal or general knowledge database.
• 😀 If a query falls outside the scope of the available databases (e.g., asking for the 2015 World Series winner), the agent can route it to a failsafe and return a message indicating the lack of information.
• 😀 Agentic RAG can be applied to industries like customer support, legal tech, and healthcare, where intelligent decision-making and context-specific data retrieval are critical.
• 😀 By enabling the LLM to make decisions about data sources and response types, Agentic RAG moves beyond simple response generation and into more advanced, context-aware decision-making, enhancing adaptability and accuracy.

Q & A

What is Retrieval Augmented Generation (RAG)?

Retrieval Augmented Generation (RAG) is a process that enhances responses generated by large language models (LLMs) by incorporating relevant data retrieved from a vector database. This extra context helps to improve the accuracy and reliability of the LLM's output.

How does the traditional RAG pipeline work?

In a traditional RAG pipeline, a user query is sent to the LLM after being interpolated into a prompt. The LLM then generates a response based solely on its pre-trained knowledge, without any external context from databases or other resources.

What role does a vector database play in the RAG pipeline?

A vector database in the RAG pipeline stores relevant data and serves as an additional context source. When a user query is received, the vector database is queried, and the retrieved data is used as context for the LLM's response, improving the relevance and accuracy of the generated answer.

What is Agentic RAG and how does it differ from traditional RAG?

Agentic RAG is an evolution of traditional RAG where the LLM acts as an intelligent agent, not only generating responses but also making decisions about which data sources to query based on the nature of the user's query. This makes the process more dynamic and adaptable compared to the static nature of traditional RAG.

How does the LLM in Agentic RAG decide which database to query?

In Agentic RAG, the LLM uses its language understanding capabilities to interpret the user's query and determine its context. Based on this understanding, the LLM decides whether to query an internal documentation database or a general knowledge base, depending on the nature of the question.

Can Agentic RAG handle queries outside of the available data sources?

Yes, Agentic RAG can recognize when a query falls outside the scope of the available data sources. In such cases, the LLM can route the query to a failsafe mechanism and provide a response such as 'Sorry, I don't have the information you're looking for.'

What are some potential applications of Agentic RAG?

Agentic RAG can be used in various fields such as customer support, legal tech, and healthcare. For example, a customer support system could use it to pull data from internal resources or general FAQs, and legal professionals could retrieve information from internal briefs or public case databases.

How does Agentic RAG improve response quality?

By using the LLM as an agent that intelligently selects the most relevant data sources for each query, Agentic RAG ensures that responses are not only accurate but also contextually appropriate. This enhances the overall quality of the generated responses.

What kind of data sources can be used in Agentic RAG?

In Agentic RAG, data sources can include internal documentation (e.g., company policies and guidelines) and general industry knowledge (e.g., public standards, best practices). The LLM agent can select the most relevant source based on the user's query.

What makes Agentic RAG more adaptable than traditional RAG systems?

Agentic RAG is more adaptable because it allows the LLM to actively decide which data sources to query, rather than just relying on static pre-determined data. This decision-making process enables the system to provide more precise and context-aware responses based on real-time data or varying contexts.