AI Architect Academy
Master the art of building intelligent AI systems from prompt engineering to production deployment
Comprehensive 35-hour program covering the complete generative AI stack. Learn to architect, build, and deploy AI-powered applications using cutting-edge technologies including OpenAI GPT-4, Claude, LangChain, vector databases, and autonomous AI agents. This course takes you from foundational concepts to advanced enterprise implementations with hands-on projects and real-world case studies.
- ✓ Prompt Engineering
- ✓ LangChain & RAG
- ✓ Fine-Tuning
- ✓ AI Agents
This course includes:
- 📹 35 hours of video content
- 📝 12 comprehensive modules
- 💻 Hands-on coding projects
- 🏆 Certificate of completion
- 🔄 Full access during your selected plan
Course Overview
What You'll Learn
Master these essential skills that employers are actively seeking
Prompt Engineering
Master few-shot learning, chain-of-thought prompting, and advanced techniques to get optimal outputs from any LLM
LangChain & RAG
Build intelligent retrieval systems using vector databases like Pinecone, Chroma, and embeddings for contextual AI
Fine-Tuning
Customize models with LoRA, QLoRA, and PEFT techniques for domain-specific performance
AI Agents
Design autonomous agents using ReAct, function calling, tool use, and multi-agent collaboration patterns
Learning Outcomes
By the end of this course, you will be able to:
Course Curriculum
12 comprehensive modules covering everything you need to know
LLM Fundamentals
- Transformer architecture
- Tokenization
- Context windows
- Temperature & sampling
Prompt Engineering Mastery
- Zero-shot & few-shot
- Chain-of-thought
- System prompts
- Output formatting
OpenAI & Anthropic APIs
- Chat completions
- Function calling
- Streaming
- Error handling
Vector Databases & Embeddings
- Pinecone
- Chroma
- Similarity search
- Chunking strategies
Building RAG Systems
- Document ingestion
- Retrieval optimization
- Reranking
- Hybrid search
LangChain Deep Dive
- Chains & agents
- Memory types
- Callbacks
- Custom tools
Fine-Tuning LLMs
- LoRA & QLoRA
- Dataset preparation
- Training pipelines
- Evaluation metrics
AI Agents & Autonomy
- ReAct pattern
- Tool use
- Planning
- Multi-agent systems
Voice & Multimodal AI
- Speech-to-text
- Text-to-speech
- Vision models
- Multimodal prompts
Production Deployment
- API design
- Caching strategies
- Cost optimization
- Monitoring
Responsible AI
- Safety guardrails
- Content filtering
- Bias mitigation
- Privacy
Capstone Projects
- AI chatbot
- RAG application
- Autonomous agent
- Custom fine-tuned model
Hands-On Projects
Build real-world, portfolio-ready applications
Intelligent Document Q&A System
Build a RAG-powered system that can answer questions from your own documents with citations
AI Customer Support Agent
Create an autonomous agent that handles customer inquiries with tool use and escalation logic
Domain-Specific Chatbot
Fine-tune an LLM for specialized knowledge in your industry or domain
Multi-Agent Research Assistant
Design a system with multiple AI agents collaborating to research and synthesize information
Tools & Technologies
Industry-standard tools you'll master in this course
📋 Prerequisites
- Basic Python programming knowledge
- Understanding of APIs and HTTP requests
- Familiarity with JSON data format
- No prior ML/AI experience required (helpful but optional)
How It Works
Understand LLMs
Learn how large language models work, their capabilities and limitations
Build Applications
Create AI-powered apps using APIs, chains, and retrieval systems
Customize Models
Fine-tune and adapt models for your specific use cases
Deploy at Scale
Launch production AI systems with monitoring and optimization
Tips for Success
Experiment with different prompting strategies - small changes can dramatically improve outputs
Understand token limits and context windows to optimize your applications
Learn to evaluate AI outputs critically - not everything an LLM says is accurate
Build projects that solve real problems - this is the best way to learn
Stay updated with the rapidly evolving AI landscape through our course updates
Frequently Asked Questions
No! This course focuses on using AI through APIs and frameworks. Basic understanding of ML concepts helps but isn't required. We explain everything from the ground up.
We cover OpenAI (GPT-4, GPT-3.5), Anthropic (Claude), Google (Gemini), and open-source models like Llama 2 and Mistral. You'll learn to work with multiple providers.
Absolutely not! While we cover ChatGPT/OpenAI extensively, this course teaches you the complete AI stack including RAG, fine-tuning, agents, and multi-modal AI across various providers.
You'll build a document Q&A system, customer support agent, domain-specific chatbot, and multi-agent research assistant. All projects are portfolio-ready.
Lifetime access! Plus you get all future updates as AI technology evolves. We continuously add new content covering the latest developments.
Absolutely! The skills you learn are directly applicable to building AI features for products, automating business processes, and creating AI-powered solutions.
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