Master Python and building robust APIs with FastAPI or Flask to serve AI models.
Understand the transformer architecture, tokenization, and how Large Language Models work.
Learn advanced prompting techniques like Chain-of-Thought, ReAct, and Few-Shot learning.
Understand vector embeddings and manage high-dimensional data with Pinecone, Chroma, or Weaviate.
Build Retrieval Augmented Generation systems to connect LLMs with private and real-time data.
Master orchestration frameworks like LangChain, LlamaIndex, or CrewAI for complex AI workflows.
Design autonomous AI agents capable of using tools, reasoning, and planning multi-step tasks.
Learn when and how to fine-tune models using PEFT techniques like LoRA and QLoRA for domain specificity.
Implement advanced search systems using hybrid search, reranking, and dense/sparse retrieval.
Deploy and monitor LLM apps with tools like LangSmith or Arize Phoenix to track evaluations and latency.