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Prompt Engineer Roadmap

Master the art and science of Prompt Engineering — from LLM fundamentals to building production-grade AI agents and RAG systems.

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1. Fundamentals of AI & LLMs

How Large Language Models Work (Transformers, Attention)

Tokens, Context Windows & Embeddings

Model Types: GPT, Claude, Gemini, LLaMA

Temperature, Top-P, Sampling Parameters

2. Prompting Fundamentals

Zero-Shot Prompting

Few-Shot Prompting

Chain-of-Thought Prompting

Role / System Prompting

Instruction-Following Best Practices

Tree of Thoughts

Self-Consistency

3. Advanced Prompting Techniques

Prompt Chaining

ReAct (Reasoning + Acting)

RAG (Retrieval-Augmented Generation)

Vector Databases (Pinecone, ChromaDB, Weaviate)

4. AI Agent Development

LangChain / LlamaIndex

Function Calling & Tool Use

Multi-Agent Systems (CrewAI, AutoGen)

Memory Systems (Short & Long-Term)

5. APIs & Integration

OpenAI / Anthropic / Google APIs

Python for AI (Requests, FastAPI)

Streaming Responses

Structured Output (JSON Mode)

Fine-tuning APIs

6. Evaluation, Safety & Deployment

Prompt Evaluation Frameworks (PromptFoo, RAGAS)

Hallucination Detection

AI Safety & Red Teaming

Cost Optimization (Token Budgets)

Deploying LLM Apps (Vercel, GCP, AWS SageMaker)

Prompt Engineer / AI Engineer