Generative AI & LLMs

Large Language Models, prompt engineering, RAG, fine-tuning, and AI application development.

Overview

The modern AI engineer roadmap for the LLM era. This roadmap covers the full spectrum of generative AI — from transformer architectures and prompt engineering through Retrieval-Augmented Generation, fine-tuning techniques, agent frameworks, and the engineering practices needed to build production AI applications. Each phase is sequenced to build practical skills progressively, starting with using LLMs effectively and advancing to customizing and deploying them.

Generative AI has transformed the technology landscape faster than any previous paradigm shift. LLMs like GPT-4, Claude, and Llama power products used by hundreds of millions of people, and every major company is building AI-native features into their products. The demand for engineers who can build with and on top of these models has exploded — AI engineer roles now outnumber traditional ML research positions by a wide margin. Whether you want to build chatbots, coding assistants, content generation tools, or enterprise knowledge systems, this skillset is the foundation.

What distinguishes this roadmap from generic AI courses is its emphasis on applied engineering over theory. You will learn prompt engineering patterns that reliably produce high-quality outputs, RAG architectures that ground LLM responses in your own data using vector databases like Pinecone and FAISS, fine-tuning techniques like LoRA and QLoRA that customize models on consumer hardware, and agent frameworks like LangChain and LlamaIndex that enable LLMs to use tools and reason over multiple steps. The roadmap also covers the critical topics most resources ignore: evaluation metrics for generative outputs, cost optimization, latency reduction, and AI safety.

By the end of this roadmap, you will be able to architect and build production AI applications, implement RAG pipelines for enterprise knowledge retrieval, fine-tune open-source models for domain-specific tasks, evaluate and monitor generative AI systems, and discuss the full LLM application stack confidently in interviews at AI-forward companies.

Interview questions

  • Do I need ML knowledge for GenAI roles?
  • Which LLM should I build with?
  • What is RAG and when should I use it?
  • How do I evaluate LLM output quality?
  • Fine-tuning vs RAG vs prompt engineering — which approach?
  • What does a GenAI interview look like?

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