What is Agentic RAG?

📚 Main Topics

Introduction to Retrieval Augmented Generation (RAG)
- Definition and purpose of RAG.
- How RAG enhances responses from large language models (LLMs).
The RAG Pipeline
- Traditional RAG process: user query, vector database, and LLM response.
- Importance of context in improving response quality.
Agentic RAG
- Introduction of an agent in the RAG pipeline.
- The agent's role in decision-making and data source selection.
Data Sources
- Internal documentation vs. general industry knowledge.
- How the agent determines which database to query based on context.
Handling Irrelevant Queries
- The agent's ability to recognize out-of-scope questions.
- Implementation of a failsafe mechanism for irrelevant queries.
Applications of Agentic RAG
- Use cases in customer support and legal tech.
- Potential for broader applications across various fields.
Future of AI Systems
- Evolution of AI systems to understand context better.
- The promise of more responsive, accurate, and adaptable AI solutions.

RAG significantly improves the reliability and accuracy of LLM responses by incorporating relevant data.
The introduction of an agent in the RAG pipeline enhances decision-making capabilities, allowing for more tailored responses.
The ability to route queries to appropriate databases based on context is crucial for effective information retrieval.
A failsafe mechanism is essential for handling irrelevant queries, ensuring user satisfaction.
Agentic RAG has vast potential applications across multiple industries, paving the way for advanced AI systems.

Understanding the context of a query is vital for delivering accurate information.
The integration of intelligent agents can transform traditional data retrieval processes into dynamic, context-aware systems.
Continuous evolution in AI technology will lead to systems that can provide significant value to users by understanding and responding to their needs effectively.