I always get asked for an AI Agent learning roadmap So if I were starting, I would start from here.. AI Agents are the new AI trend of the future However, getting started with AI Agents can be a bit overwhelming. Here I have attached a brief road map for you to get started. 📌 Level 1: Learning the Basics of GenAI and RAG 1. GenAI Introduction - Key Concepts to Learn: a. Overview of Generative AI and its applications. b. Differences between Generative and traditional AI. 2. Basics of LLMs - Key Concepts to Learn: a. Transformer architecture and attention mechanisms. b. Tokenization and embeddings. 3. Basics of Prompt Engineering - Key Concepts to Learn: a. Using zero-shot, few-shot, and chain-of-thought prompting. b. Techniques like temperature control for refining output. 4. Data Handling and Processing - Key Concepts to Learn: a. Cleaning and structuring data for training and inference. b. Preprocessing techniques like tokenization and normalization. 5. Introduction to API Wrappers - Key Concepts to Learn: a. Automating tasks using API calls. b. Basics of REST and GraphQL APIs. 6. RAG Essentials - Key Concepts to Learn: a. Basics of Retrieval-Augmented Generation (RAG). b. Embedding-based search with vector databases like ChromaDB, Milvus. 📌 Level 2: AI Agent-Focused Learning 1. Introduction to AI Agents - Key Concepts to Learn a. Agent-environment interaction. 2. Learn Agentic Frameworks - Key Concepts to Learn a. Agent workflows with frameworks like LangChain. b. Explore low-code langflow 3. Building a Simple AI Agent - Key Concepts to Learn a. Creating an agent with framework b. LLM APIs keys and integration 4. Basics of Agentic Workflow - Key Concepts to Learn: a. Break tasks into logical steps and optimize orchestration for seamless agent collaboration. b. Learning to Implement robust error recovery mechanisms 5. Learning About Agentic Memory - Key Concepts to Learn a. Short-term vs long-term memory vs episodic memory b. Storage and retrieval mechanism (vector, key-value, knowledge graph) 6. Basics of Agentic Evaluation - Key Concepts to Learn a. Measuring success metrics like accuracy and response time b. Evaluating agent decision-making and context retention 7. Basics of Multi-Agent Collaboration - Key Concepts: a. Collaboration strategies and agent dependencies b. Agent communication protocols 8. Learning Agentic RAG - Key Concepts: a. Context handling and memory b. Building agentic pipelines These are a few concepts that will help you move forward faster. I've even attached free sources for you to get started. Check them out in the comments If you are a business leader, we've developed frameworks that cut through the hype, including our five-level Agentic AI Progression Framework to evaluate any agent's capabilities in my latest book. 🔗 Book info: https://amzn.to/4irx6nI Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents | 48 comments on LinkedIn