- 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

Technical Orientation (Python, Numpy, Probability, Statistics, Tensors)

How to Use Google Colab Exercises

- 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

Introduction to Building an LLM

- 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

Tokens and Embeddings

Core math, tokens, and architectures that power today’s AI systems

Foundations & Building Blocks of Modern LLMs

- 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

Multimodal Embeddings

- 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

Neural Network Fundamentals

- 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

Attention Layer

How neural networks learn, align modalities, and reason with attention

Multimodal Intelligence, Core Networks and the Power of Attention

- 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

Synthetic Data

- 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

Advanced Prompt Engineering

- 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

Advanced RAG

These are the steps for context engineering, synthetic data, evaluations, prompts, and RAG.

Advance Context engineering

Stop Watching If You Lack These 2 Things

Why Do You Need A Workflow?

Trigger Warning

What's The Plan?

Step 1 - The Role

Step 2 - The Wish

Step 3 - The PRD

Step 4 - The Phase 1

Step 5 - The User Journey

Step 6 - The Politically Incorrect Sorting

Step 7 - The UI Development Plan

Step 8 - The Vibe

Step 9 - The UI Flow

Step 10 - The Design System

Step 11 - The Storage

Create a masterplan that contains all the information you'll need to start building a beautiful and professional application

Fullstack Planning

Your Life 1 Hour From Now

The 4 Things You Need To Know About Bolt

The Most Important Part Of Coding With Bolt

The Second Most Important Part Of Coding With Bolt

How To Write Your App's First Prompt

How To Build Your MVP Feature-by-Feature

How To Add A New Screen To Your App

How Does Bolt Handle Abstraction?

Finalizing The App + Next Steps

Learn the basics of Bolt and build a web app MVP in an hour (or less)

Vibe Coding Entire Application

Recap

What You'll Learn

What Is Supabase?

Supabase > Firebase

How To Connect Bolt With Supabase

Adding Authentication

How To Make Bolt Use 5x Fewer Tokens & Execute 5x Faster

How To Debug Your App

How to Connect, Code & Debug Supabase With Bolt

Supabase + Bolt

Read This Or Get Confused

Lesson 1 - The Philosophy You Need To Adopt

Lesson 3 - How Netlify Blows The Competition Out Of The Water

Deployment 2 - File System + Netlify UI

Deployment 3 - File System + Netlify CLI

How To Deploy Apps Faster Than Ever With Netlify

Rapid Deployment

Getting Started with n8n: Your First Step to AI Automation

Exploring AI Integration for Smart Automation

Step-by-Step Workflow

Planning an App and Setting Up Your n8n

Launching n8n and Exploring the Interface

Create Your First n8n AI Workflow

Completing Your AI Agent by Adding Tools to It

Enhancing Your AI Agent with Custom HTTP Tools

Wrapping Up Your n8n AI Agent and Exploring Next Steps

Automation with n8n

Introduction to MCP and Its Role in the Future of AI Agents

Understanding MCP’s Architecture

Diving Into MCP Servers and a Workflow Example

Building Your First MCP Server – Let’s Get Coding!

Connecting Your MCP Server to Claude for Desktop

MCP on Practice

Beat the AI Filter - Introduction

The AI Hiring Landscape

A Look at ATS (Aplicant Tracking Software)

The Fundamentals of a Strong CV

How to Get Past AI Filters

R-Strategists and K-Strategists

What about AI?

Example of an AI CV

Conclusion

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.

AI for Career

Extra materials

Orientation Session 1

Orientation Session 2

Additional Discussion on RAG: Emphasis on Systemic Way of Approaching/Experimenting RAG Application

AI in Production

AI Agents Patterns

A Glimpse into our Group Coaching Calls 1

A Glimpse into our Group Coaching Calls 2

A Glimpse into our Group Coaching Calls 3

A Glimpse into our Group Coaching Calls 4

A Glimpse into our Group Coaching Calls 5

Most developers begin by calling GPT with a wrapper or simple API, and at first everything seems to work until the failures appear: unstable outputs, inconsistent reasoning, broken tool calls, looping agents, and RAG pipelines that retrieve the wrong information. These problems happen because modern AI applications rely on multiple layers working together, not just a prompt. 
This program teaches advanced context engineering by breaking down that full stack into its core components. You learn how prompts shape model behavior and why they fail when not paired with evidence or structure. You explore tokens, embeddings, transformer internals, and multimodal representations so you understand how models encode meaning. You study neural network blocks and attention so you see how 
context is formed and why certain inputs dominate a model’s decision process. You then learn how RAG pipelines act as the model’s external memory and how to design indexing, chunking, reranking, and query routing so retrieval actually improves accuracy. You work with synthetic data to fix failure modes, generate edge cases, and build evaluation sets that make the system more reliable. You analyze model errors 
through scoring pipelines and LLM-as-judge evaluations to reveal patterns that normal testing misses. You learn how agents use action-observation loops, how tool calling works, and why agents break when context or evidence is incomplete. You practice connecting all these pieces into real workflows so the model, prompt, tools, and retrieval operate as one system. You also learn full-stack execution: 
planning features, building UIs, wiring Supabase, orchestrating automation, and deploying end-to-end applications. The goal is to help you build AI products where behavior is predictable, debuggable, and measurable. By understanding the full stack, you stop guessing why an LLM behaves a certain way and start controlling its inputs, constraints, and evidence. Every module reinforces how these layers depend on each other, 
giving you the ability to design, evaluate, and ship AI systems that actually work in production.

From Prompt to Product in Days

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.

Power AI course 

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 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

Learn

The newline Guide to Building Your First GraphQL Server with Node and TypeScript

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Amelia Wattenberger

Author of Fullstack D3

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