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GenAI
Generative AI, large language models, prompt engineering, RAG systems, and AI application development.
1Roadmaps
3Notes
- beginner01
LLM Fundamentals
Understand transformer architecture, tokenization, attention mechanisms, and how large language models work.
Transformer ArchitectureTokenizationAttention MechanismPre-trainingResources
- beginner02
Prompt Engineering
Master prompt design patterns: few-shot, chain-of-thought, system prompts, and structured output generation.
Few-Shot PromptingChain of ThoughtSystem PromptsOutput ParsingResources
- intermediate03
RAG Systems
Build Retrieval-Augmented Generation pipelines. Vector databases, embedding models, chunking strategies, and retrieval optimization.
Vector DatabasesEmbeddingsChunking StrategiesHybrid SearchResources
- advanced04
Fine-Tuning & RLHF
Fine-tune models with LoRA/QLoRA, RLHF pipelines, and evaluation frameworks for domain-specific tasks.
LoRA & QLoRARLHFEvaluation MetricsDataset CurationResources
- advanced05
AI Agents & Tool Use
Build autonomous AI agents with tool calling, multi-step reasoning, memory systems, and orchestration frameworks.
Agent ArchitectureTool CallingMemory SystemsMulti-AgentResources