LLM Engineering

RAG and Agentic AI Roadmap

Standards	SLNo.	Description	Projects
Level 1	1	Gen AI: Overview
	2	Open Source and Closed Source AI
	3	NLP Tasks: Summarization, Translation, Conversation, Sentiment Analysis, Topic Modeling
	4	Prompt Engineering - Basics: Zero Shot, One Shot, Few Shot
	5	Tokenization
	6	Embeddings
	7	Search
Level 2	8	Langchain, Llama Index
	9	Vector Databases: Qdrant, Milvus, ChromaDB
	10	Python Frameworks: FastAPI, Flask, Streamlit, Gradio
	11	Prompt Engineering - Advanced: CoT, ReAct
	12	Knowledge Base: PDF, CSV, Excel, Text, PPT, Word, Databases, Websites
	13	Chunking Methodologies
Level 3	14	Re-Ranker: Cross Encoders
	15	MultiQuery Expansion
	16	Metadata Filtering
	17	Hybrid Search (BM25)
	18	Memory Based Retrieval
	19	Summarization / Compression of Larger Docs
Level 4	20	Tool Calling, Function Calling
	21	Agents: Zero Shot Agents, ReAct Agents, Plan and Execute Agents
	22	Tool Kit: Calculator, Websearch, Code Interpreter
	23	Persona Based Agents / Role Playing Agents: Product Manager, Doctor
	24	Multi Agent Frameworks: LangGraph, Agno, AutoGen, CrewAI
	25
	26
	27
	28