AI Systems & LLM Application Engineering
Move beyond superficial chatbot wrappers. Learn to engineer robust Retrieval-Augmented Generation (RAG), autonomous multi-agent tool calling, and production latency optimization.
Learn how to force LLMs to output strict, schema-validated JSON with Pydantic & Zod, ensuring zero broken frontend UI layouts or invalid database inserts.
Master document chunking strategies, semantic embedding models, hybrid keyword-vector search with pgvector, and cross-encoder re-ranking.
How to Navigate Your Learning Journey
Watch the Visual Primer
Start with our curated video tutorial to build a mental picture of how data and components flow before looking at complex code.
Read the Official Reference
Read the recommended official documentation sections. Focus on understanding the core APIs, standard error shapes, and patterns.
Apply the 70/30 Rule
Spend 70% of your study time building with your own keyboard, and only 30% passively watching. Typing real code is how instincts are formed.
Ship the Checkmark Project
Build the small standalone project for each phase. Once functional, tick it off in your interactive milestone tracker below to save progress.
Milestone Checklist & Progress
Your progress is saved in your browser. Check off items as you build.
LLM APIs, Structured Output & Tool Calling
Vector Databases & Advanced RAG Architectures
Multi-Agent Systems & Autonomous Workflows
Capstone: Autonomous Enterprise Knowledge Copilot
LLM APIs, Structured Output & Function Calling
Goal: Interact with leading frontier models (Gemini 2.5, Claude 3.5, OpenAI) using native SDKs, streaming responses, and function tool execution.
Core Skills to Master
- Token Economics & Context Windows: Calculating input/output token pricing, context caching, and rate limit handling with exponential backoff.
- Structured Outputs: Enforcing strict Pydantic / Zod models on model completions.
- Function / Tool Calling: Teaching LLMs to trigger database lookups, weather APIs, and calculation scripts dynamically.
- Streaming SSE: Delivering real-time token streams to Next.js frontends via Server-Sent Events (SSE).
High-Yield Reading
Practical Video Resource
Vector Databases & Advanced RAG Architectures
Goal: Ground language models on proprietary company PDFs, databases, and codebases to eliminate hallucinations.
Core Skills to Master
- Chunking Strategies: Semantic chunking, recursive character splitting, and markdown header chunking.
- Embeddings: Generating vectors with OpenAI, Voyage, or Google text-embedding models.
- Vector Indexing with pgvector: Using PostgreSQL HNSW indexes for high-speed cosine and euclidean similarity lookups.
- Re-ranking: Using Cohere ReRank or cross-encoders to refine retrieved documents before prompt injection.
High-Yield Reading
Multi-Agent Systems & Autonomous Workflows
Goal:Build autonomous multi-agent pipelines (Planner → Researcher → Coder → Reviewer) with memory and guardrails.
Core Skills to Master
- Agent Architectures: ReAct loops (Reasoning + Acting), LangGraph state graphs, and CrewAI delegation.
- Persistent Memory: Short-term session state vs. long-term memory stored in vector stores and relational DBs.
- Safety & Guardrails: Input sanitization, prompt injection defenses, and cost anomaly alerts.
High-Yield Reading
Practical Video Resource
The Capstone: Autonomous Knowledge Copilot & Research Agent
Architect an enterprise-ready AI copilot that ingests company documentation and performs multi-step research tasks:
System Specifications:
- Automatic ingestion pipeline converting PDF, DOCX, and web URLs into vector embeddings in pgvector.
- Hybrid search (PostgreSQL full-text tsvector + cosine distance embeddings) with re-ranking.
- Interactive agent capable of browsing documentation, summarizing findings, and generating cited output.
- Real-time token streaming and interactive UI artifacts rendered on Next.js 16.
- Observability dashboard tracking token expenditure, latency metrics, and user feedback ratings.
High-Yield Reading
Practical Video Resource
Coursera & Accredited AI Certifications
Strengthen your foundation with rigorous, industry-defining certifications from Stanford, DeepLearning.AI, and IBM.
Generative AI with Large Language Models
Instruction fine-tuning, PEFT/LoRA, RLHF alignment, and deploying LLMs on AWS SageMaker.
Machine Learning Specialization (Andrew Ng)
The gold standard foundation: gradient descent, neural networks, unsupervised learning, and recommender systems.
IBM Applied AI Professional Certificate
Build AI-powered chatbots, computer vision pipelines, and intelligent assistant microservices.
Get the Production Boilerplate & Project Files
We are currently preparing full, production-tested GitHub starter repositories and weekly checkmark guides for AI Systems & LLM Application Engineering. Join the early access circle to receive the code templates when they drop.
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