Trustworthy RAG & Autonomous AI Agents: Build Retrieval, Reasoning & Tool-Using Systems
From RAG to Reliable AI Agents — Retrieve Knowledge, Reason with Context, Use Tools & Act with Trust.
About This Course
This 3-day virtual hands-on workshop introduces participants to Retrieval-Augmented Generation (RAG), autonomous AI agents, and trustworthy AI systems. Participants will learn to connect LLMs with external knowledge, vector databases, tools, and APIs for grounded and reliable outputs. The workshop covers semantic and hybrid retrieval, agent memory, tool calling, reasoning, workflow orchestration, and RAG evaluation. It also addresses hallucination mitigation, guardrails, observability, and human-in-the-loop validation. Through guided hands-on sessions, participants will build and evaluate a practical RAG-powered autonomous AI assistant.
Aim
The aim of this workshop is to equip participants with practical skills to build grounded RAG systems and autonomous AI agents. It also focuses on improving reliability through evaluation, hallucination mitigation, guardrails, and trustworthy AI practices.
Workshop Objectives
- Understand the fundamentals of Retrieval-Augmented Generation, LLM grounding, and knowledge retrieval.
- Build RAG pipelines using embeddings, vector databases, semantic search, and hybrid retrieval.
- Develop autonomous AI agents with tool calling, memory, reasoning, and workflow orchestration.
- Evaluate AI systems for factuality, faithfulness, hallucinations, and retrieval quality.
- Apply trustworthy AI practices, including guardrails, observability, and human-in-the-loop validation.
Workshop Structure
📅 Day 1: Retrieval-Augmented Generation & Knowledge Grounding
- Introduction to Large Language Models and knowledge limitations
- Hallucinations, outdated knowledge, and domain-specific information gaps
- Fundamentals of Retrieval-Augmented Generation
- Document ingestion, chunking, embeddings, and indexing
- Semantic search, keyword retrieval, and hybrid retrieval
- Vector databases and similarity search
- Metadata filtering and reranking
- Prompt construction with retrieved context
- Citation-aware and source-grounded responses
- Introduction to knowledge graphs and advanced RAG architectures
🛠️ Hands-on:
- Load and preprocess domain-specific documents
- Generate embeddings and create a searchable knowledge base
- Build a basic RAG question-answering system
- Compare keyword search, semantic retrieval, and hybrid retrieval
- Generate grounded answers with supporting context and citations
🧰 Tools Covered:
Google Colab, Python, LangChain/LlamaIndex, Hugging Face, Sentence Transformers, FAISS/Chroma, BM25, and LLM APIs
📅 Day 2: Autonomous AI Agents, Tool Use & Reasoning
- From conversational AI to autonomous AI agents
- Core components of an agent: model, tools, memory, state, and planning
- Agentic RAG and retrieval as an AI-agent capability
- Tool/function calling and structured outputs
- Reasoning, task decomposition, and multi-step workflows
- ReAct-style reasoning and action cycles
- Short-term and persistent agent memory
- Agent routing and workflow orchestration
- Self-reflection, verification, and adaptive decision making
- Single-agent and multi-agent architectures
- Human-in-the-loop AI systems
- Applications in research, enterprise automation, analytics, and scientific problem solving
🛠️ Hands-on:
- Convert the Day 1 RAG pipeline into a tool-using AI agent
- Add document search, calculation, and structured-data tools
- Implement tool selection and task routing
- Build a multi-step reasoning workflow
- Add memory and a verification/reflection step
- Develop a prototype autonomous research or enterprise assistant
🧰 Tools Covered:
Google Colab, Python, LangGraph, LangChain/LlamaIndex, LLM function calling, Pydantic-style structured outputs, FAISS/Chroma, and APIs
📅 Day 3: Trustworthy AI, Evaluation & Agent Reliability
- Principles of trustworthy and responsible AI
- Factuality, faithfulness, grounding, and answer relevance
- Hallucination detection and mitigation
- Retrieval-quality and generation-quality evaluation
- Context precision, context recall, and answer correctness
- Uncertainty, confidence, and calibration
- Prompt injection and adversarial RAG
- Tool misuse and unsafe agent behaviour
- Bias, privacy, fairness, and responsible AI
- Explainability and traceable agent workflows
- Human-in-the-loop evaluation
- Guardrails, auditing, and observability
- Evaluation benchmarks for RAG and agents
- Trustworthy deployment in research, healthcare, finance, education, and enterprise systems
🛠️ Hands-on:
- Build an evaluation dataset for the RAG-agent system
- Compare baseline LLM, RAG, and agentic-RAG responses
- Evaluate retrieval relevance, groundedness, and faithfulness
- Detect unsupported or hallucinated responses
- Add verification, reranking, and guardrails
- Trace agent actions and tool calls
- Prepare a final trustworthy AI evaluation report
🧰 Tools Covered:
Google Colab, Python, RAGAS/DeepEval, LangGraph, LangSmith-style tracing, FAISS/Chroma, reranking models, pandas, and evaluation templates
Who Should Enrol?
- Graduate and Postgraduate Students in Computer Science, AI, Data Science, IT, Software Engineering, Electronics, and related fields
- PhD Scholars and Researchers working in LLMs, NLP, RAG, knowledge systems, autonomous agents, or trustworthy AI
- Academicians and Faculty Members interested in Generative AI, intelligent systems, research applications, and curriculum development
- Industry Professionals such as AI/ML engineers, data scientists, software developers, solution architects, and automation professionals
- Participants with basic Python knowledge are preferred; prior experience with RAG or AI agents is not mandatory
Important Dates
Registration Ends
October 20, 2026
IST 4: 30 PM
Workshop Dates
October 20, 2026 – October 22, 2026
IST 5:30 PM
Workshop Outcomes
- Understand the architecture and limitations of modern LLM-based systems.
- Build a complete Retrieval-Augmented Generation pipeline using domain-specific documents.
- Implement semantic, keyword, and hybrid information retrieval.
- Create grounded AI responses with supporting evidence and citations.
- Transform a conventional RAG system into a tool-using autonomous AI agent.
- Design multi-step reasoning and task-decomposition workflows.
- Integrate external tools, databases, and APIs with AI agents.
Fee Structure
Student Fee
₹2199 | $60
Ph.D. Scholar / Researcher Fee
₹3299 | $70
Academician / Faculty Fee
₹4499 | $90
Industry Professional Fee
₹5999 | $110
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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