Power AI course
Advanced Context Engineering is the spine of this course: you are not just learning prompts, you are learning a full context stack that runs from tokens and embeddings through attention, synthetic data, evaluation, and RAG. We start at the numerical layer (vectors, tensors, neural networks, attention) then climb up through tokens, embeddings, multimodal representations, and transformer internals so you see exactly how context is encoded, routed, and used for prediction. On top of that foundation you learn how to design prompts as controllable programs, generate and curate synthetic data, apply axial coding and LLM-as-judge evaluation to detect failure patterns, and build RAG systems that act as real search indices across vector databases, APIs, SQL, and the web.
By the end, you have a rare, end-to-end context engineering skill set: you know how to shape model behavior with prompts, stress-test it with synthetic data, debug it with evaluations, and wrap it in advanced RAG so that you can plug everything into a full-stack AI system for real products. This depth and sequencing of content is usually scattered across research papers, internal company docs, and niche talks, but here it is integrated into one coherent stack that is very hard to find in a single course.
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22 hrs 3 mins
78 Videos
zaoyang
Owner of \newline and previously co-creator of Farmville (200M users, $3B revenue) and Kaspa ($3B market cap). Self-taught in gaming, crypto, deep learning, now generative AI. Newline is used by 250,000+ professionals from Salesforce, Adobe, Disney, Amazon, and more. Newline has built editorial tools using LLMs, article generation using reinforcement learning and LLMs, instructor outreach tools. Newline is currently building generative AI products that will be announced soon.
01Remote
You can take the course from anywhere in the world, as long as you have a computer and an internet connection.
02Self-Paced
Learn at your own pace, whenever it's convenient for you. With no rigid schedule to worry about, you can take the course on your own terms.
03Community
Join a vibrant community of other students who are also learning with Power AI course . Ask questions, get feedback and collaborate with others to take your skills to the next level.
04Structured
Learn in a cohesive fashion that's easy to follow. With a clear progression from basic principles to advanced techniques, you'll grow stronger and more skilled with each module.
From Prompt to Product in Days
How to think like an LLM by understanding tokens, embeddings, and context flow
How to read, shape, and debug model inputs using tokenizer design
How to manipulate embeddings for similarity, clustering, and retrieval
How to build multimodal embeddings that align text, images, audio, and video
How to understand attention, QKV mechanics, and contextual weighting
How to modify transformer layers and experiment with the internal architecture
How to run an LLM inference pipeline from raw text to next-token generation
How to design high-precision prompts that control reasoning and output stability
How to engineer multi-step prompts for Chain-of-Thought, PAL, and multi-agent reasoning
How to secure prompts using XML tags, defensive structures, and anti-jailbreak patterns
How to extract model failures using LLM-as-Judge, error scoring, and evaluation metrics
How to apply axial coding to cluster errors into actionable improvement paths
How to generate synthetic data to fill domain gaps, create edge-cases, and improve accuracy
How to create synthetic stress tests that expose RAG and prompt weaknesses early
How to choose between prompting, RAG, or fine-tuning depending on the problem
How to chunk, embed, and index text into a retrieval system that acts like a custom search engine
How to build hybrid retrieval combining vector search, metadata, SQL, APIs, and web search
How to design reranker-driven pipelines that push the right chunk to the top
How to rewrite queries and context to correct vague, multi-hop, or ambiguous inputs
How to evaluate retrieval quality using recall, relevance, and chunk-level diagnostics
How to stitch retrieved evidence into faithful, citation-driven generation
How to combine prompts, synthetic data, evaluations, and RAG into a full-stack AI system
Building an AI product today feels exciting, but also overwhelming. New tools appear every week, and while they promise speed and power, they rarely tell you how to connect everything into a working app. Most people can write a prompt. Fewer can turn that prompt into a real product with a backend, automations, deployment, and a clean launch. And that’s where things usually fall apart.
You might have tried stitching together tutorials, only to end up with half-finished experiments. Or maybe you’ve built small demos, but nothing production-ready. The truth is that modern AI development isn’t hard, it’s fragmented. You need prompting, but also storage. You need automation, but also deployment. And now there are AI agents, embeddings, and new standards like MCP changing the landscape again.
Meanwhile, people who can ship fast are launching apps in days and grabbing opportunities before the rest of the world even finishes reading documentation. The difference isn’t talent, it’s having a clear path from idea to product.
So how do you actually turn a simple prompt into something real? How do you decide what to build, wire up your backend, automate workflows, and push it live without getting stuck?
In this course, we break down that entire process. You’ll learn the core AI foundations, practical prompt engineering, lightweight RAG, full-stack planning, Supabase + Bolt integration, workflow automation with n8n, fast deployment with Netlify, and a forward look at MCP and the future of AI agents. By the end, you’ll have built and shipped a complete AI-powered product and know exactly how to do it again.
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Course Syllabus and Content
Foundations & Building Blocks of Modern LLMs
4 Lessons2 Hours 44 Minutes
Core math, tokens, and architectures that power today’s AI systems
- How AI Thinks in Numbers: Dot Products and Matrix Logic
- NumPy Power-Tools: The Math Engine Behind Modern AI
- Introduction To Machine Learning Libraries
- Two and Three Dimensional Arrays
- Data as Fuel: Cleaning, Structuring, and Transforming with Pandas
- Normalization in Data Processing: Teaching Models to Compare Apples to Apples
- Probability Foundations: How Models Reason About the Unknown
- The Bell Curve in AI: Detecting Outliers and Anomalies
- Evaluating Models Like a Scientist: Bootstrapping, T-Tests, Confidence Intervals
- Transformers: The Architecture That Gave AI Its Brain
- Diffusion Models: How AI Creates Images, Video, and Sound
- Activation Functions: Teaching Models to Make Decisions
- Vectors and Tensors: The Language of Deep Learning
- GPUs, Cloud, and APIs: How AI Runs in the Real World
- Intuition for decoder-only LLMs
- Tokens, embeddings, transformer pipeline
- Autoregressive next-token generation
- Generative AI modalities overview
- Diffusion vs transformer model families
- Inference flow and prompt processing
- Build a real LLM inference API
- Architecture: attention, context, decoding
- Training phases: pretrain to RLHF
- Vertical vs generic LLM design
- Distillation, quantization, efficient scaling
- Reasoning models: Chain of Thought and Test Time Compute
- Hands on Exercises
- Tokenization as dictionary for model input
- Tokens → IDs → contextual embeddings
- Semantic meaning emerges only in embeddings
- Transformer layers reshape embeddings by context
- Pretrained embeddings accelerate domain understanding
- Good tokenization reduces loss, improves learning
- Tokenizer choice impacts RAG chunking
- Compression tradeoffs differ by domain needs
- Tokenization affects inference cost and speed
- Compare BPE, SentencePiece, custom tokenizers
- Emerging trend: byte-level latent transformers
- Generations of embeddings add deeper semantics
- Similarity measured via dot products, distance
- Embeddings enable search, clustering, retrieval systems
Multimodal Intelligence, Core Networks and the Power of Attention
3 Lessons2 Hours 10 Minutes
How neural networks learn, align modalities, and reason with attention
- Foundations of multimodal representation learning
- Text, image, audio, video embeddings
- Contrastive learning for cross-modal alignment
- Shared latent spaces across modalities
- Vision encoders and patch tokenization
- Transformer encoders for text meaning
- Audio preprocessing and spectral features
- Time-series tokenization via SAX or VQ
- Fusion modules for modality alignment
- Cross-attention for integrated reasoning
- Zero-shot retrieval and multimodal search
- Real-world multimodal applications overview
- Feedforward networks as transformer core
- Linear layers for learned projections
- Nonlinear activations enable expressiveness
- SwiGLU powering modern FFN blocks
- MLPs refine token representations
- LayerNorm stabilizes deep training
- Dropout prevents co-adaptation overfitting
- Skip connections preserve information flow
- Positional encoding injects word order
- NLL loss guides probability learning
- Encoder vs decoder architectures explained
- FFNN + attention form transformer blocks
- Why context is fundamental in LLMs
- Limits of n-grams, RNNs, embeddings
- Self-attention solves long-range context
- QKV: query–key–value mechanics
- Dynamic contextual embeddings per token
- Attention weights determine word relevance
- Multi-head attention = parallel perspectives
- GQA reduces attention compute cost
- Mixture-of-experts for specialized attention
- Editing and modifying transformer layers
- Decoder-only vs encoder–decoder framing
- Building context-aware prediction systems
Advance Context engineering
3 Lessons2 Hours 39 Minutes
These are the steps for context engineering, synthetic data, evaluations, prompts, and RAG.
- Intro to Synthetic Data and Why It Matters in Modern AI
- What Synthetic Data Really Is vs Common Misconceptions
- How Synthetic Data Fills Gaps When Real Data Is Limited or Unsafe
- The Synthetic Data Flywheel: Generate → Evaluate → Iterate
- Using Synthetic Data Across Pretraining, Finetuning, and Evaluation
- Synthetic Data for RAG: How It Stress-Tests Retrieval Systems
- Fine-Tuning with Synthetic Examples to Update Model Behavior
- When to Use RAG vs Fine-Tuning for Changing Information
- Building RAG Systems Like Lego: LLM + Vector DB + Retrieval
- How Vector Databases Reduce Hallucinations and Improve Accuracy
- Generating Edge Cases, Adversarial Queries, and Hard Negatives
- Control Knobs for Diversity: Intent, Persona, Difficulty, Style
- Guardrails and Bias Control Using Prompt Engineering and DPO
- Privacy Engineering with Synthetic Data for Safe Testing
- Debugging AI Apps Using Synthetic Data Like a Developer Debugs Code
- LLM-as-Judge for Fast, Cheap, Scalable Data Quality Checks
- Axial Coding: Turning Model Failures Into Actionable Error Clusters
- Evaluation-First Loops: The Only Way to Improve Synthetic Data Quality
- Components of High-Quality Prompts for Synthetic Data Generation
- User Query Generators for Realistic Customer Support Scenarios
- Chatbot Response Generators for Complete and Partial Solutions
- Error Analysis to Catch Hallucinations, Bias, and Structure Failures
- Human + LLM Evaluation: Combining Experts With Automated Judges
- Model Cards and Benchmarks for Understanding Model Capabilities
- Intro to Prompt Engineering and Why It Shapes Every LLM Response
- How Prompts Steer the Probability Space of an LLM
- Context Engineering for Landing in the Right “Galaxy” of Meaning
- Normal Prompts vs Engineered Prompts and Why Specificity Wins
- Components of a High-Quality Prompt: Instruction, Style, Output Format
- Role-Based Prompting for Business, Coding, Marketing, and Analysis Tasks
- Few-Shot Examples for Teaching Models How to Behave
- Synthetic Data for Scaling Better Prompts and Personalization
- Choosing the Right Model Using Model Cards and Targeted Testing
- When to Prompt First vs When to Reach for RAG or Fine-Tuning
- Zero-Shot, Few-Shot, and Chain-of-Thought Prompting Techniques
- PAL and Code-Assisted Prompting for Higher Accuracy
- Multi-Prompt Reasoning: Self-Consistency, Prompt Chaining, and Divide-and-Conquer
- Tree-of-Thought and Branching Reasoning for Hard Problems
- Tool-Assisted Prompting and External Function-Calling
- DSPy for Automatic Prompt Optimization With Reward Functions
- Understanding LLM Limitations: Hallucinations, Fragile Reasoning, Memory Gaps
- Temperature, Randomness, and How to Control Output Stability
- Defensive Prompting to Resist Prompt Injection and Attacks
- Blocklists, Allowlists, and Instruction Defense for Safer Outputs
- Sandwiching and Random Enclosure for Better Security
- XML and Structured Tagging for Reliable, Parseable AI Output
- Jailbreak Prompts and How Attackers Trick Models
- Production-Grade Prompts for Consistency, Stability, and Deployment
- LLM-as-Judge for Evaluating Prompt Quality and Safety
- Cost Optimization: How Better Prompts Reduce Token Usage
- Intro to RAG and Why LLMs Need External Knowledge
- LLM Limitations and How Retrieval Fixes Hallucinations
- How RAG Combines Search + Generation Into One System
- Fresh Data Retrieval to Overcome Frozen Training Cutoffs
- Context Engineering for Giving LLMs the Right Evidence
- Multi-Agent RAG and Routing Queries to the Right Tools
- Retrieval Indexes: Vector DBs, APIs, SQL, and Web Search
- Query Routing With Prompts and Model-Driven Decision Logic
- API Calls vs RAG: When You Need Data vs Full Answers
- Tool Calling for Weather, Stocks, Databases, and More
- Chunking Long Documents Into Searchable Units
- Chunk Size Trade-offs for Precision vs Broad Context
- Metadata Extraction to Link Related Chunks Together
- Semantic Search Using Embeddings for Nearest-Neighbor Retrieval
- Image and Multimodal Handling for RAG Pipelines
- Text-Based Image Descriptions vs True Image Embeddings
- Query Rewriting for Broad, Vague, or Ambiguous Questions
- Hybrid Retrieval Using Metadata + Embeddings Together
- Rerankers to Push the Correct Chunk to the Top
- Vector Databases and How They Index Embeddings at Scale
- Term-Based vs Embedding-Based vs Hybrid Search
- Multi-Vector RAG and When to Use Multiple Embedding Models
- Retrieval Indexes Beyond Vector DBs: APIs, SQL, Search Engines
- Generation Stage: Stitching Evidence Into Final Answers
- Tool Calling With Multiple Retrieval Sources for Complex Tasks
- Synthetic Data for Stress-Testing Retrieval Quality Early
- RAG vs Fine-Tuning: When to Retrieve and When to Update the Model
- Prompt Patterns for Retrieval-Driven Generation
- Evaluating Retrieval: Recall, Relevance, and Chunk Quality
- Building End-to-End RAG Systems for Real Applications
Fullstack Planning
15 Lessons 35 Minutes
Create a masterplan that contains all the information you'll need to start building a beautiful and professional application
Vibe Coding Entire Application
9 Lessons 25 Minutes
Learn the basics of Bolt and build a web app MVP in an hour (or less)
Supabase + Bolt
8 Lessons 41 Minutes
How to Connect, Code & Debug Supabase With Bolt
Rapid Deployment
5 Lessons 15 Minutes
How To Deploy Apps Faster Than Ever With Netlify
Automation with n8n
9 Lessons 49 Minutes
Building AI-Powered Workflows
MCP on Practice
5 Lessons1 Hours 6 Minutes
The Future of AI Agents
AI for Career
9 Lessons 43 Minutes
Beat the AI Filter, a short course that looks at how AI is used in hiring and how you can stand out in this competitive landscape.
Extra materials
11 Lessons9 Hours 52 Minutes
Extra materials
Meet the Course Instructor
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Frequently Asked Questions
What is Power AI Course?
In this course we’ll cover how to take a simple prompt idea and turn it into a full AI product in days. We walk through AI fundamentals, practical prompt engineering, lightweight RAG, full-stack planning, Supabase + Bolt integration, workflow automation with n8n, rapid deployment with Netlify, and an introduction to MCP and the future of AI agents. We’ll build a complete end-to-end AI app with authentication, storage, automation, and live deployment. This project is valuable because it gives you a repeatable system for shipping real AI products quickly without needing deep ML expertise.
Who is this course for?
This course was produced for builders who want to turn AI ideas into real products quickly. It’s ideal for developers, tech-savvy founders, indie hackers, and anyone comfortable with basic web tools who wants a clear, practical path from prompt to deployment. No advanced ML background is required — just the desire to build and ship fast.
What if I don't like the course?
We offer a 30-day money-back guarantee, so if you're not satisfied with the course, you can request a refund within 30 days of purchase by sending us a message.
What is included in the course?
This course include 67 videos, totaling 12 hours. You’ll have access to every lesson video, textual lesson content, downloadable project code files, interactive IDE, and AI Tutor.
What are there prerequisites for this course?
This course assumes you know the basics of web development, can navigate simple JavaScript or TypeScript files, and are comfortable following setup instructions for tools like Supabase, Bolt, or Netlify. You don’t need advanced AI or machine learning experience — we cover the essential foundations inside the course. As long as you can read code, run simple commands, and have built a small project before, you’re ready.
How long will it take to complete the course?
The course offers flexibility, allowing you to learn at your own pace. Start, stop, re-watch anytime. It’s expected that you’d spend approximately 20 hours going through the entire course materials.
Can I access the course on my mobile device?.
Yes, the course is fully responsive and can be accessed on your mobile device.
Is there a certificate upon completion of the course?
Yes, you can get a certificate by sending us a message.
Can I download the course videos?
No, the course videos cannot be downloaded, but they can be accessed online at any time.
What is the price of the course?
The course is currently priced at [$2,499 USD].
How is this course different than other content available online?
This course is unlike any other course on building AI products because it doesn’t drown you in theory or scatter information across dozens of disconnected tutorials. Instead, it gives you a clear, end-to-end path from prompt to fully deployed product. You learn foundations, prompting, RAG, Supabase, Bolt, n8n, deployment, and even MCP — all in one place — with a practical workflow you can reuse for every future project. You also get access to the instructor for one-on-one guidance if you get stuck. The benefit is simple: you won’t just understand AI concepts, you’ll actually ship real AI products quickly and confidently.
