The complete hands-on course — from "what is AI?" to building production-grade LLM applications. 25 sections covering text models, image generation, agents, RAG, evals, and real-world deployment.
What You'll Learn
Who It's For
Prompt engineering is a foundational interface skill that matters across every technical role — not a niche "prompt engineer" job title. This course is built for professionals who want to work with AI effectively.
Employer-Aligned Skill Clusters
This course maps directly to four skill clusters that employers are hiring for right now. Each cluster builds real capability — not just awareness.
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01▼AI-Assisted Knowledge WorkAccelerate real tasks with structured prompt patterns
The ability to reliably get high-quality output from AI models — for research synthesis, document generation, content production, and decision support — has become a baseline expectation across professional roles. This cluster teaches you to move beyond ad-hoc prompting to structured, repeatable patterns that deliver predictable results.
You'll master the five principles of prompting, standard text model practices, and advanced techniques like chain-of-thought, few-shot prompting, and role assignment. You'll also work with real model interfaces: ChatGPT, Claude, and the OpenAI API.
Five Principles of Prompting Chain-of-Thought Few-Shot Examples Role & Persona Assignment Instruction Formatting Output Structuring Document Synthesis ChatGPT Deep Dive Claude & Gemini APIsCourse sections: 01–05, 08, 19, 22, 23 -
02▼AI-Enabled Software EngineeringIntegrate AI into every stage of the dev workflow
Modern software engineering increasingly involves directing, reviewing, and correcting AI-generated code. This cluster covers GitHub Copilot workflows, OpenAI's code-focused API features, and the practical patterns for using AI effectively in a real engineering context — including when not to trust the output.
You'll work with Copilot in real coding sessions, explore the OpenAI function-calling API for code tasks, and learn how to structure prompts that produce maintainable, testable code rather than plausible-looking noise.
GitHub Copilot Copilot Chat OpenAI Code API Function Calling Code Review Prompts Test Generation Debugging with AI Refactoring PatternsCourse sections: 06, 08, 24 -
03▼LLM Application EngineeringTool calling, RAG, evals, observability — production-grade
This is the highest-demand technical cluster — covering the full stack for building LLM-powered applications that actually ship. You'll build RAG pipelines from scratch using embeddings and vector databases, implement tool-calling agents, and use LangChain and LangGraph to orchestrate multi-step workflows.
Critically, this cluster emphasizes production competence over demo competence: you'll implement evaluation discipline with systematic evals that catch regressions before they reach users, and learn observability patterns to monitor what your LLM app is actually doing in production.
🔌 MCP (Model Context Protocol) — This course covers the emerging MCP standard for integrating AI models with external tools, services, and data sources. MCP is becoming the dominant integration pattern across LLM platforms and is directly applicable to production deployments.Embeddings Vector Databases RAG Pipelines Tool Calling MCP Standard LangChain LangGraph Agent Architectures Prompt Evals Regression Testing ObservabilityCourse sections: 07, 09, 10, 11, 17, 18 -
04▼Governed Deployment & RiskSecrets handling, auditability, responsible AI in production
As AI systems move from experiment to production, organizations need engineers and practitioners who can identify risks, implement controls, and maintain auditability. This cluster bridges technical implementation and governance — covering the patterns that separate a demo from a trusted production system.
Topics include: secrets management in LLM apps, prompt injection and defense, data privacy in RAG systems, evaluation discipline as an ongoing practice, and the governance frameworks emerging from regulatory guidance on AI deployment.
Secrets Management Prompt Injection Defense Data Privacy in RAG Audit Trails Eval Discipline AI Risk Frameworks Output Monitoring Responsible DeploymentCourse sections: 17, 18, 19, 22, 25
Full Curriculum — 25 Sections
Organized into five learning phases. Estimated total: 60–80 hours of instruction, labs, and projects. Sections marked CODING include hands-on code.
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01▼IntroductionCourse overview, goals, and how to get the most from it
Set up your environment and understand what this course will and won't do. Covers the current AI landscape, how to think about prompt engineering as a career skill (not just a job title), and how the 25 sections fit together into a coherent learning arc.
~1 hr AI Landscape Overview Course Roadmap Environment Setup -
02▼Five Principles of PromptingThe mental model behind every effective prompt
A framework that applies across every model and use case: clarity, context, constraints, examples, and iteration. These five principles are the foundation everything else in this course builds on.
~2 hrs Clarity & Specificity Context Injection Constraints Few-Shot Examples Iterative Refinement -
03▼How Does AI WorkTokens, transformers, and training — the minimum viable theory
You don't need a PhD, but understanding how LLMs process input helps you design better prompts and avoid common failure modes. Covers tokenization, attention mechanisms, training vs. inference, temperature, and why models hallucinate.
~2.5 hrs Tokenization Transformer Architecture Temperature & Top-P Hallucination Sources Context Windows -
04▼Deep Dive on ChatGPTInside the world's most-used AI model
A thorough exploration of ChatGPT's interface, model variants, system prompts, memory, plugins, and the GPT builder. Understand its strengths and limitations compared to other frontier models, and how to apply the five principles in practice.
~2.5 hrs GPT-4o vs o1 System Prompts Memory Features Custom GPTs Plugins & Tools
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05▼Standard Text Model PracticesRepeatable patterns for professional tasks
The workhorse section for knowledge workers. Covers the prompt patterns used for document drafting, research synthesis, summarization, Q&A, classification, and structured data extraction. Learn when to use zero-shot vs few-shot vs chain-of-thought.
~3 hrs Zero-Shot Prompting Few-Shot Prompting Chain-of-Thought Summarization Classification Data Extraction -
06▼OpenAI Features & FunctionalityAPI, function calling, and structured outputs — with code
Move beyond the chat interface and into the API. Covers authentication, the completions and chat endpoints, function calling, structured JSON outputs, streaming, and the assistants API. Hands-on Python throughout.
~3.5 hrs CODING OpenAI API Function Calling JSON Mode Streaming Assistants API -
07▼Embeddings and Vector DatabasesThe engine behind semantic search and RAG
Embeddings convert text into numerical vectors that capture semantic meaning. This section teaches how they work, how to generate them with the OpenAI API, and how to store and query them in vector databases like Pinecone and Chroma for building RAG systems.
~4 hrs CODING Embedding Models Cosine Similarity Pinecone ChromaDB Semantic Search RAG Foundations -
08▼Advanced Text Model TechniquesReAct, self-consistency, meta-prompting, and more
Beyond the basics — advanced prompting techniques used in production LLM systems: ReAct (reason + act), self-consistency sampling, constitutional AI patterns, meta-prompting, and prompt chaining. Includes code examples for each technique.
~3.5 hrs CODING ReAct Pattern Self-Consistency Meta-Prompting Prompt Chaining Constitutional AI -
11▼AI Text Model ProjectsBuild real apps with everything you've learned so far
Apply your text model skills in guided end-to-end projects: a document Q&A assistant, an automated research summarizer, and a structured data extraction pipeline. Each project includes evaluation criteria and a code walkthrough.
~4 hrs CODING Document Q&A App Research Summarizer Data Extraction Pipeline Project Evaluation
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12▼Deep Dive on Midjourney v6Master the most powerful image generation platform
Comprehensive Midjourney coverage: parameter mastery (aspect ratios, stylize, chaos, seed), prompting strategies for photorealism vs. illustration, style references, character references, and the v6 vs. previous versions breakdown.
~3 hrs MJ Parameters Prompt Anatomy Style Reference Character Reference Upscaling -
13▼Standard Image Model PracticesUniversal techniques across all image generation tools
Platform-agnostic prompt engineering for image models: composition, lighting, style keywords, negative prompting, and aspect ratio strategies. These techniques transfer across Midjourney, DALL-E, Stable Diffusion, and emerging tools.
~2 hrs Composition Lighting Keywords Style Transfer Negative Prompts Iterative Refinement -
14▼Advanced Image Generation TechniquesInpainting, outpainting, ControlNet, and batch workflows
Professional-level image generation workflows: inpainting for targeted edits, outpainting to extend images, ControlNet for pose/structure control, image-to-image transformation, and batch processing for consistent visual assets.
~2.5 hrs Inpainting Outpainting ControlNet Image-to-Image Batch Workflows -
15▼AI Image Model ProjectsBuild a portfolio with real deliverables
Guided projects applying your image generation skills: brand identity asset creation, consistent character set for a story, and a product mockup workflow. Each project ships a real portfolio deliverable.
~2.5 hrs Brand Assets Character Consistency Product Mockups Portfolio Output -
16▼Deep Dive on Stable Diffusion XLLocal, uncensored, fully customizable image generation
Run Stable Diffusion XL locally with AUTOMATIC1111 or ComfyUI. Covers model setup, LoRA fine-tuning, VAE selection, ControlNet integration, and the SDXL vs. SDXL Turbo vs. FLUX comparison. Ideal for creative professionals needing full control.
~2 hrs SDXL Setup AUTOMATIC1111 ComfyUI LoRA VAE FLUX -
21▼Deep Dive on DALL-E 3OpenAI's image model — API integration and capabilities
DALL-E 3 via ChatGPT and directly through the API. Covers prompt rewriting behavior, quality/size parameters, image editing endpoints, and how DALL-E 3 compares to Midjourney and SDXL for different use cases.
~2 hrs DALL-E 3 API Prompt Rewriting Image Editing Model Comparison
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09▼Deep Dive on LangChainThe most widely used LLM application framework
LangChain from the ground up: chains, memory, retrievers, tools, and agents. Build a complete RAG-powered Q&A system over your own documents. Understand when LangChain adds value and when it adds complexity you don't need.
~4 hrs CODING Chains Memory Retrievers Tools LangChain Agents RAG App -
10▼Deep Dive on LangGraphStateful, cyclical agent workflows
LangGraph extends LangChain for building agents that can loop, branch, and maintain state across turns. Build a ReAct agent with persistent memory, implement human-in-the-loop checkpoints, and understand when graph-based orchestration is the right choice.
~4 hrs CODING Graph Architecture State Management Conditional Edges Human-in-the-Loop Multi-Agent Systems -
18▼Agent ArchitecturesPatterns for building reliable autonomous systems
A systems-level look at agent design: single-agent vs. multi-agent, orchestrator/worker patterns, tool use and MCP integration, error recovery, and deployment considerations. Covers emerging production patterns including structured outputs for reliable tool calling.
~4 hrs CODING Agent Patterns Orchestrator/Worker MCP Integration Error Recovery Tool Reliability Production Deployment -
24▼Deep Dive on GitHub CopilotAI pair programming in your actual workflow
Practical GitHub Copilot usage for working engineers: autocomplete patterns, Copilot Chat for explanation and refactoring, test generation, slash commands, and the workspace agent. Includes a live coding session demonstrating Copilot in a real feature build.
~4 hrs CODING Copilot Autocomplete Copilot Chat Test Generation Slash Commands Workspace Agent
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17▼Prompt Optimization & EvalsMove from vibes to measurement — evaluation discipline for production
The most underrated skill in the field. Build systematic evaluation pipelines that measure prompt quality, catch regressions, and give you confidence before shipping. Covers LLM-as-judge patterns, human eval design, automated test suites, and the Braintrust / PromptFoo ecosystem.
~3 hrs CODING Eval Design LLM-as-Judge Regression Testing Braintrust PromptFoo Dataset Curation -
19▼Deep Dive on Anthropic ClaudeThe model built for safety, reasoning, and production reliability
Deep exploration of the Claude model family: Constitutional AI, the Claude API, extended thinking (Claude's chain-of-thought mode), tool use patterns, and MCP server integration. Covers Claude 3.5/3.7 Sonnet capabilities and when to choose Claude over GPT-4.
~3 hrs Constitutional AI Extended Thinking Claude API MCP Servers Model Selection -
20▼Deep Dive on Google Veo3AI video generation — the emerging frontier
Google Veo3 represents the frontier of AI video generation. This section covers text-to-video prompting techniques, camera motion descriptors, scene consistency, and how video generation fits into creative production workflows alongside still image tools.
~2 hrs Text-to-Video Camera Motion Scene Consistency Video Prompting Veo3 vs Sora -
22▼Deep Dive on Other AI ModelsGemini, Llama, Mistral, Cohere, and the open-source landscape
The AI model landscape is fragmented and fast-moving. This section gives you a structured framework for evaluating models across: capability, cost, latency, context window, multimodal support, and open-source vs. proprietary. Covers Gemini 1.5/2, Llama 3, Mistral, and Cohere.
~2.5 hrs Gemini 2 Llama 3 Mistral Cohere Model Selection Framework Open-Source vs. Proprietary -
23▼AI Tools We've TriedHonest reviews of the broader AI tool ecosystem
A curated, honest review of the broader AI tooling ecosystem: writing assistants, coding tools, research tools, productivity apps, and specialized vertical AI. Organized by use case with practical assessments of what's actually worth using vs. what's hype.
~1.5 hrs Writing Tools Research AI Productivity Apps Vertical AI Tool Evaluation -
25▼ConclusionPutting it all together and what comes next
Review the full learning arc, map your new skills to real job roles and cert paths, and build a personal AI development roadmap. Includes guidance on maintaining skills as the field evolves, responsible use principles, and the TechNodeX community resources available to you.
~2 hrs Skill Mapping Cert Paths Career Roadmap Responsible AI Community
Certification Alignment
This course builds real working knowledge aligned with three leading AI/ML certifications. Studying for these certs after this course is a natural next step — not a separate grind.
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