Here’s a roadmap that breaks it all down into 3 easy levels:
Level 1: GenAI & RAG Basics
1- GenAI Introduction: Get familiar with what Generative AI is and where it’s used.
2- Basics of LLMs: Understand how large language models are trained & used.
3- Prompt Engineering: Learn to write effective prompts for better LLM results.
4- LLM Parameters: Control outputs with temperature, top-p, and token settings.
5- Data Preprocessing: Clean, chunk, and format data for AI tasks.
6- RAG Fundamentals: Combine LLMs with search to retrieve accurate info.
7- Vector Databases: Store & search embeddings using Pinecone, Chroma, etc.
8- API Wrappers: Interact with LLMs using LangChain, LlamaIndex, or direct APIs.
9- Tool Integration: Let LLMs call tools like search, code, or APIs.
Level 2: AI Agent Essentials
10- What Are AI Agents?: Learn how agents plan, reason, and act autonomously.
11- Agentic Frameworks: Explore LangChain, CrewAI, AutoGen, and more.
12- Build Your First Agent: Create a simple AI agent that performs real tasks.
13- Agent Workflows: Design how agents think, act, and complete tasks.
14- Agent Memory: Add memory so agents can recall past actions.
15- Agent Evaluation: Track agent accuracy, performance, and reliability.
16- Multi-Step Reasoning: Teach agents to think in logical sequences.
17- Multi-Agent Systems: Enable agents to work together on complex tasks.
18- Agentic RAG: Use RAG in an autonomous agent setup.
19- Action Planning: Make agents plan, adapt, and retry intelligently.
20- Safety & Guardrails: Add filters to keep agents safe and factual.
Level 3: Advanced Agent Skills
21- Real-World Integration: Connect agents to tools like Slack, Notion, or Gmail.
22- Autonomous Loops: Create agents that run and update tasks on their own.
23- Custom Toolkits: Equip agents with APIs or Python tools.
24- Optimize Performance: Improve speed, cost, and error handling.
25- Deploy to Production: Host your AI agent for real users to access.
Graphic credit: Python_Dv (X)
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