The abstract for "Foundations of Agentic AI – Your First RAG Assistant" describes the development and principles behind building a simple, agentic question-answering assistant using Retrieval-Augmented Generation (RAG) techniques. The project showcases how an agentic AI system can leverage both retrieval from custom document sets and generative capabilities to autonomously process queries, plan tasks, and deliver contextually relevant answers. It integrates core agentic workflows—such as planning, tool use, and iterative decision-making—demonstrating foundational concepts in agent-based AI that learns and adapts from user interactions. This approach highlights how combining structured retrieval with agentic intelligence produces more flexible and adaptive AI assistants
The introduction to "Foundations of Agentic AI – Your First RAG Assistant" presents the module as the initial hands-on project in the Agentic AI Developer Certification Program, aiming to ground learners in the real-world application of Retrieval-Augmented Generation (RAG) with agentic principles. Participants are not expected to know the implementation details upfront; rather, the introduction sets expectations for learning the foundational skills and tools needed to build a question-answering assistant over the course of the module. The project emphasizes using frameworks such as LangChain and vector stores like FAISS or Chroma to connect prompts, retrievers, and generative responses, forming the basic workflow of an agentic RAG assistant.
The related work for "Foundations of Agentic AI – Your First RAG Assistant" examines the evolution of Retrieval-Augmented Generation (RAG), traditional AI agents, and their convergence in the agentic RAG paradigm. RAG systems emerged to address the limitations of large language models (LLMs) by enabling real-time grounding to external data sources, thus improving relevance and factual accuracy for domain-specific applications. Significant research has outlined the modular RAG framework, which integrates retrieval, generative models, and flexible components for increased adaptability and precision.
The project guides you to build a question-answering assistant using a RAG (Retrieval-Augmented Generation) framework.
Core pipeline: Prompt formulation → vector store retrieval (using FAISS or Chroma) → LLM-generated response.
Documents are ingested into the vector store to be used for retrieval.
Interface can be a CLI, notebook, or minimal web UI.
Example queries are used for qualitative evaluation of the system’s retrieval and response quality.
Run queries against your assistant using your chosen custom document set (e.g., technical docs, Wikipedia subset).
Test different questions to evaluate retrieval accuracy, generation quality, and overall user interaction.
Optionally, implement enhancements like session-based memory or intermediate reasoning (ReAct, Chain-of-Thought).
The RAG assistant demonstrates the ability to answer queries contextually from the supplied knowledge base.
Effective in providing concise, relevant responses based on retrieval quality and LLM capabilities.
Performance can be further improved by experimenting with prompt engineering, vector store configuration, and incorporating memory or reasoning steps.
Highlights the benefits of retrieval-augmented generation for domain-specific Q&A tasks.
Discusses trade-offs: retrieval precision vs. generation creativity, and pipeline complexity vs. flexibility.
Notes on user experience (UX), scalability, and potential limitations (e.g., out-of-domain queries).
The project shows that integrating retrieval and generation in an agentic framework provides a practical and extensible approach to building adaptive assistants.
Sets the foundation for more advanced agent-based AI systems with autonomy and reasoning capabilities.
LangChain Documentation
FAISS/Chroma Vector Store Documentation
Ready Tensor Platform Publications
(External documentation or academic papers as used)
Ready Tensor platform for infrastructure and resources.
Project contributors and peer reviewers for guidance and feedback.