Become an LLM Engineer: Complete Course
Course Curriculum
13 modules designed to master the subject.
Module 1: Introduction to LLM Engineering
Role, responsibilities, and the evolving ecosystem of LLM engineering.
The Role of an LLM Engineer: Defining the Future of AI Development
Discover what it means to be an LLM Engineer in the modern era. Learn the core competencies, the difference between AI Engineers and MLEs, and how to navigate the emerging AI stack.
Career Outlook and Industry Demand for LLM Engineers
Explore the explosive growth of the LLM Engineering field. Understand salary trends, high-demand industries, and the skills that will make you indispensable in the AI-driven economy.
Core Responsibilities of an LLM Engineer
Master the four pillars of the LLM Engineering lifecycle: System Design, Agent Development, Production Deployment, and Continuous Monitoring. Learn the professional standards for shipping AI.
The LLM Ecosystem: Navigating the AI Tech Stack
Get a comprehensive bird's-eye view of the modern LLM ecosystem. Learn the difference between model providers, orchestration frameworks, and agentic platforms like LangChain, LangGraph, and CrewAI.
Module 2: Foundations of Machine Learning, NLP, and Deep Learning
Transformers, attention mechanisms, and the neural foundations of LLMs.
Machine Learning vs. Deep Learning: The AI Evolution
Understand the fundamental difference between classical Machine Learning and modern Deep Learning. Learn how the shift from feature engineering to neural networks paved the way for LLMs.
The DNA of LLMs: Tokenization, Embeddings, and Attention
Master the three core concepts of Natural Language Processing (NLP) that make LLMs possible. Learn how text becomes numbers, how numbers gain meaning, and how attention allows models to focus.
Neural Network Essentials for LLM Engineers
Demystify the biology-inspired mathematics behind LLMs. Learn about neurons, weights, biases, and the training loop (Backpropagation) that allows models to learn from text.
The Transformer: The Architecture That Changed Everything
Understand the revolutionary Transformer architecture introduced in 'Attention Is All You Need'. Learn about Encoders, Decoders, and why parallel processing unlocked the era of LLMs.
Module 3: Python for LLM Engineering
Advanced Python, async programming, and data processing for AI.
Python Essentials for LLM Engineering
Master the Python features that are critical for AI development. Learn about Pydantic for validation, environment management, and the specific syntax needed to build robust agentic systems.
Async Programming for High-Performance AI
Learn how to build responsive AI applications using Python's asyncio. Understand how to handle slow model APIs, parallelize retrieval, and prevent your UI from freezing during long-running agent tasks.
Data Manipulation and Preprocessing for LLMs
Master the art of 'Garbage In, Garbage Out'. Learn how to clean raw text, handle problematic encodings, and structure data for optimized RAG and fine-tuning pipelines.
Scalable Python for AI: Architecting Production Systems
Move beyond scripts. Learn how to architect maintainable AI applications using Object-Oriented principles, Factory patterns, and clean separation of concerns for models, tools, and state.
Module 4: Prompt Engineering
Systematic prompt design, CoT, and safety guardrails.
The Art and Science of Prompt Engineering
Master the fundamental principles of prompt engineering. Learn how to transform vague instructions into precise, deterministic results by understanding the model's 'attention' and 'reasoning' boundaries.
Advanced Prompting: Zero-Shot to Chain-of-Thought
Unlock the reasoning power of LLMs. Master the three most powerful techniques in the engineer's toolkit: Zero-Shot for speed, Few-Shot for consistency, and Chain-of-Thought for complex logic.
System Prompts, Personas, and Safety Guardrails
Learn to build the foundation of an agent's character. Master the use of System Prompts to define behavior, Personas to ensure tone, and Guardrails to prevent malicious interactions.
Iterative Prompt Design: The Engineering Workflow
Stop 'guessing' and start 'engineering'. Learn the systematic workflow for testing, evaluating, and refining your prompts for production accuracy and safety.
Module 5: Retrieval-Augmented Generation (RAG) Systems
Vector databases, semantic retrieval, and context-aware generation.
Foundations of RAG: Beyond the Model's Knowledge
Discover why RAG (Retrieval-Augmented Generation) is the backbone of production AI. Learn how to give LLMs a 'long-term memory' using your private data while reducing hallucinations.
Connecting LLMs to External Knowledge Bases
Master the data pipeline for RAG. Learn how to ingest, parse, and chunk documents from various sources like S3, Google Drive, and local file systems for AI retrieval.
Vector Databases: The Long-Term Memory of AI
Compare the leaders in the vector storage market. Learn how to choose between Chroma (Local), Pinecone (Serverless), and Weaviate (Self-hosted) for your production RAG system.
The Retrieval Loop: Query Analysis to Injection
Master the final stage of RAG. Learn how to optimize user queries, retrieve the best context, and inject it into the prompt without causing 'hallucination drift' or token overflow.
Module 6: Fine-Tuning and Model Adaptation
LoRA, QLoRA, and adapting models for specialized domain tasks.
When to Fine-Tune: Specializing Your Foundation Model
Master the decision-making process for model adaptation. Learn the difference between knowledge retrieval (RAG) and behavior adaptation (Fine-tuning), and why you shouldn't jump to retraining too early.
PEFT: LoRA and QLoRA Explained
Learn how to fine-tune massive models without needing a supercomputer. Master LoRA (Low-Rank Adaptation) and QLoRA, the techniques that allow for high-performance model adaptation on a single GPU.
Dataset Preparation: The Fuel for Fine-Tuning
Master the most critical part of the fine-tuning process. Learn how to curate, clean, and format instruction datasets that transform model behavior while avoiding 'catastrophic forgetting'.
Evaluating Fine-Tuned Models: Beyond Word Match
Master the metrics of AI performance. Learn how to use Perplexity, ROUGE, and LLM-as-a-Judge to measure if your fine-tuning was a success or a hallucination-filled failure.
Module 7: LLM Agents and Orchestration
Building autonomous systems with planning, memory, and tool use.
Introduction to AI Agents: Transitioning from Chat to Agency
Discover the paradigm shift from passive chatbots to autonomous AI agents. Learn the definition of an agent, the core components of agency, and why reasoning loops are the future of software.
The ReAct Pattern: Reasoning and Acting in Unison
Master the fundamental logic loop of autonomous AI. Learn how the ReAct (Reason + Act) pattern allows agents to solve complex tasks through observation and self-correction.
Multi-Agent Systems: Orchestrating the Swarm
Move beyond single agents. Learn how to build collaborative teams of AI agents using LangGraph and CrewAI. Master delegating, state management, and multi-agent coordination.
Human-in-the-Loop: Building Safe Agentic Workflows
Master the safety mechanisms of AI agency. Learn how to implement 'Interrupt' patterns, human approval steps, and time-travel debugging to ensure your agents remain under human control.
Module 8: Inference Optimization
Quantization, pruning, and low-latency scaling techniques.
Inference Optimization: Quantization and Pruning
Make your AI models fast and affordable. Master the techniques of Quantization (FP16 to INT4) and Pruning to shrink model size without sacrificing intelligence.
Model Serving: Deploying AI at Scale
Master the infrastructure of AI. Learn the difference between managed inference (AWS Bedrock) and self-hosted inference (vLLM, TGI). Discover how to handle thousands of concurrent requests.
Low-Latency Scaling: Speed as a Feature
Master the techniques of high-speed AI responses. Learn about KV Caching, Speculative Decoding, and load balancing across multi-GPU clusters to reduce Time-To-First-Token (TTFT).
Continuous Benchmarking: Monitoring Performance
Master the art of real-time AI observability. Learn to track latency, token usage, cost, and hallucination rates in a live production environment.
Module 9: LLMOps (MLOps for LLMs)
CI/CD, monitoring, and governance for production AI.
CI/CD for LLM Applications: Automated AI Pipelines
Learn how to build a continuous integration and deployment pipeline for AI. Master the art of automated prompt testing, model evaluation, and safe version rollouts.
Production Evaluation: Monitoring Semantic Quality
Master the art of real-time AI quality control. Learn how to use automated judges and user feedback loops to identify hallucinations and drift in your live application.
Monitoring and Logging: The AI Observability Stack
Master the tools of AI observability. Learn how to implement structured logging with OpenTelemetry and trace agentic paths with LangSmith to debug complex production failures.
Version Control for AI: Beyond Git
Learn how to manage the lifecycle of your AI components. Master versioning for prompt templates, model IDs, and vector indices to ensure reproducibility and rollback capability.
Module 10: Security, Safety, and Responsible AI
Mitigating prompt injection and ensuring ethical AI deployment.
AI Security: Prompt Injection and Jailbreaking
Protect your AI from malicious actors. Learn how to identify and mitigate prompt injection, jailbreaking, and social engineering attacks designed to override your agent's instructions.
Bias and Fairness: Ethical AI Engineering
Understand the unintentional harms of AI. Learn how to identify societal bias in training data, measure disparate impact, and implement 'Debiasing' techniques to ensure fair AI outcomes.
AI Privacy and Data Protection: Secrets in the Context Window
Protect your data at the speed of AI. Learn how to implement PII (Personally Identifiable Information) masking, private model hosting, and safe RAG pipelines that respect user privacy.
The Ethics of Engineering: Responsible AI
Move beyond the code. Explore the societal impact of your work, from job displacement concerns to the environmental cost of massive GPU clusters. Learn to build AI with a conscience.
Module 11: Cloud Integration and Scaling
Architecting scalable LLM applications on AWS and Kubernetes.
LLMs on AWS: Bedrock vs. SageMaker
Master the AWS AI landscape. Learn when to use the serverless convenience of AWS Bedrock and when to leverage the full control of Amazon SageMaker for your LLM workloads.
Kubernetes for AI: Orchestrating GPU Clusters
Master the deployment of AI at scale. Learn how to use Kubernetes to manage GPU resources, scale agentic workloads, and ensure high availability for self-hosted LLM services.
Serverless AI: Computing without Servers
Learn how to build lightweight AI applications using serverless functions like AWS Lambda and Cloudflare Workers. Master the art of 'Ephemeral AI' for cost-effective microservices.
Global AI Scaling: Multi-Region Architectures
Build AI applications for the global stage. Learn how to navigate data residency laws, route traffic across worldwide GPU clusters, and implement multi-region failover for mission-critical AI.
Module 12: Advanced Topics in LLM Engineering
Multimodal AI, long-term memory, and emerging research trends.
Multimodal AI: Teaching Machines to See and Hear
Expand your AI's senses. Learn how to build applications that process images, analyze videos, and respond to audio prompts using state-of-the-art multimodal models like GPT-4o and Gemini 1.5 Pro.
Long-Term Memory: Giving AI a Persistent Soul
Move beyond the context window. Learn how to implement persistent memory using Redis, Zep, and Mem0 to allow your agents to remember user preferences, history, and facts across months of interaction.
Self-Healing Systems: Autonomous AI Reliability
Build agents that can fix themselves. Learn how to implement self-correction, automated debugging, and self-healing pipelines that allow AI systems to recover from code and tool failures without human help.
The Future of LLM Engineering: 2026 and Beyond
Stay ahead of the curve. Explore the emerging research in LLM Engineering, from Large Action Models (LAMs) to On-Device AI and the quest for true Artificial General Intelligence (AGI).
Module 13: Capstone Project
Build and deploy a full-featured LLM-powered enterprise application.
Capstone: The Autonomous Research Assistant
Put your skills to the test. In this final module, you will build a production-ready, multi-agent RAG system that researches, summarizes, and cites sources for complex technical questions.
Designing the Research Graph
Map out the brains of your assistant. Learn how to architect a multi-agent graph with planning nodes, retrieval nodes, and quality-control loops using LangGraph.
Implementing the Research Assistant
Turn your design into a working system. In this lesson, we write the Python code for the research nodes, integrate the tools, and handle the asynchronous logic of a multi-agent system.
Deployment and Final Presentation
Launch your masterpiece. Learn how to deploy your Capstone to the cloud, run a final evaluation on your agent's performance, and present your findings like a professional LLM Engineer.
Course Overview
Format
Self-paced reading
Duration
Approx 6-8 hours
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