infographics: AI engineering

 Post | LinkedIn

Based on the shared LinkedIn post by AI Hub, the infographic outlines a structured GenAI Engineering Roadmap created by Sumit Gupta.

It breaks down the learning path for Generative AI into three progressive levels comprising 40 essential skills, moving from foundational concepts to production-ready intelligent systems.

🟢 Level 1: Beginner — Foundation & Reliability

Goal: Build reliable AI-powered applications by understanding how models work and mastering core integration.

• Models & providers & Tokens, context windows
  
  
• Prompt engineering & Temperature & sampling
  
  
• APIs, SDKs & streaming & Python / JSON / HTTP
  
  
• Structured outputs & Function calling
  
  
• Input/output validation & Prompt-injection awareness
  
  
• Cost & latency basics & Testing & logging
  
  

🟡 Level 2: Intermediate — Knowledge Systems & Production Apps

Goal: Connect AI to your own data and build applications people can trust.

• Embeddings, RAG, Chunking, & Vector databases
  
  
• Metadata & permissions, Vector search, & Hybrid search & reranking
  
  
• Document ingestion
  
  
• Tool workflows & Workflow orchestration
  
  
• FastAPI / Web apps
  
  
• Evaluation, Observability, Guardrails & access control, & Human approval flows
  
  

🔴 Level 3: Advanced — Agentic Systems & Infrastructure

Goal: Build, optimize, and scale production-ready AI systems.

• ReAct & planning patterns, State, memory & compaction
  
  
• MCP & tool ecosystems, Multi-agent coordination
  
  
• GraphRAG & Adversarial testing
  
  
• LoRA / QLoRA, DPO / RLHF, & Model routing
  
  
• vLLM & inference serving, Quantization, Semantic/KV caching
  
  
• MoE, CUDA / kernel optimization