AI Engineer Core Track : The role of the AI Engineer has shifted from building traditional machine learning models to architecting sophisticated applications around Large Language Models (LLMs).
This core track is designed to provide the foundational and advanced skills needed to transform LLMs from simple chatbots into production-ready, highly accurate, and customized autonomous tools.
1. LLM Engineering Foundations and Workflow
LLM Engineering involves the entire lifecycle of an LLM application, focusing on maximizing performance, reliability, and cost-efficiency.
Prompt Engineering & System Messages: Mastering advanced prompting techniques like Zero-Shot, Few-Shot, and Chain-of-Thought to guide LLM behavior. A critical focus is on defining the System Message to enforce persona, constraints, and output format.
Core Frameworks: Gaining proficiency with industry-standard orchestration frameworks:
LangChain/LlamaIndex: For chaining LLM calls, managing complexity, and integrating tools.
Hugging Face Ecosystem: For accessing, loading, and managing a wide variety of open-source models and datasets.
API Integration: Learning to connect LLMs to external data sources, proprietary APIs, and web services using Function Calling to extend the model’s capabilities beyond its training data.
2. Knowledge Augmentation with RAG
Retrieval-Augmented Generation (RAG) is the most critical technique for building factual, up-to-date, and domain-specific LLM applications while minimizing “hallucinations.”
RAG Architecture: Learn to design the end-to-end RAG pipeline, which involves:
Data Ingestion: Loading and processing documents.
Text Chunking: Strategically splitting documents into manageable sections for retrieval.
Embedding Generation: Using models (e.g., modern transformer models) to convert text chunks and user queries into dense numerical vectors.
Vector Database Management: Storing and indexing vectors in specialized Vector Stores (e.g., Pinecone, ChromaDB) for efficient similarity search. * Optimization: Implementing advanced RAG strategies such as Hybrid Search (combining keyword and vector search) and Re-Rankers to ensure the most relevant context is passed to the LLM.
3. Model Customization with QLoRA Fine-Tuning
While RAG injects external knowledge, fine-tuning adapts the model’s fundamental behavior, tone, and format. QLoRA (Quantized Low-Rank Adaptation) is the key to making this practical on consumer hardware.
Parameter-Efficient Fine-Tuning (PEFT): Understand the family of methods (including LoRA) that allow you to modify a tiny subset of the model’s parameters while keeping the vast majority of the original weights frozen. This dramatically reduces computational cost.
QLoRA: This technique combines LoRA with 4-bit Quantization.
4-bit Quantization: Reduces the memory footprint of the massive LLM weights by storing them in a highly compressed format (4-bit Normalized Float), allowing very large models to fit on a single, affordable GPU.
Training: Only the small LoRA adapters are updated during training, leading to significant speed and memory efficiency while achieving performance comparable to full fine-tuning.
4. Architecting Autonomous Agents
The final stage is moving from single-turn applications to building AI Agents—systems that can reason, plan, and execute multi-step tasks autonomously.
Agent Core Loop: Designing the basic Agent architecture: Plan, Act, Observe, Reflect.
Function/Tool Calling: Teaching the agent to intelligently select the right tools (e.g., calculator, web search, code interpreter) from a library and use them to accomplish its goal.
Multi-Agent Collaboration: An introduction to orchestrating teams of specialized agents (e.g., a “researcher” agent and a “writer” agent) using frameworks like AutoGen or CrewAI to solve complex problems more effectively than a single model.
Mastering this core curriculum prepares the AI Engineer to build, customize, and deploy the next generation of intelligent, autonomous applications.
