★ INTERMEDIATE COURSE
🧠

AI Integration
for Developers

Build real AI-powered applications from scratch. Master LLM APIs, embeddings, RAG pipelines, autonomous agents, and production deployment.

14
Lessons + Projects
6h
Total Duration
2
Final Projects
100%
Free Forever

Course Curriculum

5 phases from API basics to production AI systems

1
Foundations
Lessons 1-3 • LLM APIs, prompt engineering basics
LESSON 01
The AI Integration Landscape
What AI integration means for developers. Overview of LLM APIs: OpenAI, Anthropic, Gemini. Model comparison and capability matrix.
OpenAIAnthropicOverview
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LESSON 02
Getting Started with OpenAI API
API key setup, authentication, first API call in Python and curl. Chat completions, tokens, pricing, rate limits.
PythonAPITokens
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LESSON 03
Prompt Engineering for Developers
System prompts, few-shot examples, chain-of-thought, structured output. Prompting for JSON, XML. Injection risks.
PromptsJSONSecurity
Start lesson →
2
Building Apps
Lessons 4-5 • Chat apps, function calling, streaming
LESSON 04
Building Chat Applications
Multi-turn conversations, context window management, conversation pruning. FastAPI backend with streaming SSE responses.
FastAPISSEContext
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LESSON 05
Function Calling & Tool Use
JSON schema for tools, parallel function calls, tool results. Build an agent that queries weather, runs calculations, searches the web.
ToolsAgentsJSON Schema
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3
Semantic AI
Lessons 6-8 • Embeddings, vector DBs, RAG pipelines
LESSON 06
Embeddings & Semantic Search
What embeddings are (vectors), cosine similarity. Build semantic search with text-embedding-3-small and NumPy.
VectorsNumPySimilarity
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LESSON 07
Vector Databases
Pinecone, ChromaDB, Weaviate, pgvector compared. CRUD on vector stores, indexing, similarity search. ChromaDB local example.
ChromaDBPineconepgvector
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LESSON 08
RAG: Retrieval-Augmented Generation
Solve hallucination with RAG. The full pipeline: chunk, embed, store, retrieve, generate. Complete naive RAG in 30 lines of Python.
RAGChunkingRetrieval
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4
Advanced Systems
Lessons 9-12 • Agents, LangChain, streaming, production
LESSON 09
AI Agents & Autonomous Systems
LLM + memory + tools + planning. ReAct loop from scratch. OpenAI Assistants API. Memory types: in-context, vector, episodic.
ReActMemoryPlanning
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LESSON 10
LangChain & LangGraph Frameworks
LangChain LCEL pipelines, LangGraph stateful agents with cycles and branching. When to use frameworks vs raw API.
LangChainLangGraphLCEL
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LESSON 11
Streaming, Caching & Performance
SSE streaming, prompt caching, rate limit handling with exponential backoff, async batching to reduce latency.
StreamingCachingAsync
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LESSON 12
Deploying AI Apps to Production
Serverless vs container vs edge. API key security, logging, monitoring, cost estimation for production workloads.
DeployMonitoringCosts
Start lesson →
5
Capstone Projects
2 full-stack AI projects to build your portfolio
PROJECT 1
Build a Full-Stack AI Chatbot
FastAPI backend + HTML/JS frontend. Streaming responses, multi-turn conversation, system prompt configuration, token counting.
FastAPIStreamingFull-Stack
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PROJECT 2
Build a RAG Knowledge Base
Ingest PDF files, embed with OpenAI, store in ChromaDB, answer questions with citations. Complete production-ready RAG system.
RAGChromaDBPDF
Start project →