roadmap and resources to become a 1->100 gen-ai engineer

this 30-day roadmap is for intermediate developers who want to master llm application development. it covers everything from api setup and llm integration to advanced topics like rag, agent workflows, and testing. focused, practical, and straight to the point, it's designed to get you building production-ready llm apps fast.

week 1: foundation & basic llm integration

days 1-2: setup & basic api

• fastapi basics
• async programming
• building endpoints for llm interactions
• api key management, error handling, streaming responses

days 3-4: llm fundamentals

• understanding tokens, context windows, temperature
• exploring llm models (gpt-4, claude, gemini)
• implementing model switching
• model selection, cost optimization, response handling

days 5-7: prompt engineering basics

• system vs user prompts
• few-shot learning, chain of thought
• structured outputs (json, xml)
• building a prompt template system

week 2: advanced llm patterns

days 8-10: retrieval-augmented generation (rag)

• vector databases (pinecone, weaviate)
• chunking strategies and embedding models
• hybrid search techniques
• building a rag system with multiple retrieval strategies

days 11-12: advanced rag & caching

• semantic caching
• parent-child chunking
• recursive retrieval and multi-query retrieval
• adaptive rag system

days 13-14: llm output processing

• json mode and structured outputs
• output parsing and validation
• error recovery strategies
• response filtering

week 3: testing & agents

days 15-17: llm testing framework

• unit testing llm applications

• testing prompts and templates

• evaluating llms using metrics (rouge, bleu)

• setting up automated testing pipelines

days 18-21: ai agents & workflows

• agent frameworks (langchain, autogen)
• agent memory and state management
• multi-agent orchestration
• building a research agent with multiple tools

week 4: advanced features & capstone

days 22-24: advanced features

• conversation memory patterns
• custom tool development
• rate limiting and queuing
• cost optimization

days 25-27: capstone project

• building an ai-powered research assistant
• implementing rag with multiple knowledge bases
• conversation memory with context persistence
• integrating specialist agents for different tasks

days 28-30: polish & deploy

• completing test suite and performance optimization
• adding monitoring and logging
• preparing documentation and deploying to a cloud platform

best practices to follow

• always test llm outputs
• monitor token usage and optimize
• handle errors gracefully with retry logic
• document prompt templates and use version control