🔥 Level 2: Build AI Systems That Actually Work

Master Production AI Systems

🧠

RAG Architecture

Build Retrieval Augmented Generation systems. Ground AI in your private data with 95% accuracy like Thomson Reuters.

🗄️

Vector Databases

Master Pinecone, Weaviate, FAISS. Handle billion-vector searches with sub-100ms latency at enterprise scale.

🤖

Intelligent Agents

Build agents that use tools, make decisions, and orchestrate complex workflows. ReAct patterns and function calling.

⚡

Performance Optimization

Caching, batching, and semantic search. Reduce costs by 90% with smart optimization strategies.

Real Production Code

RAG System with Vector Search

import pinecone
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA

class ProductionRAGSystem:
    def __init__(self, pinecone_key: str, openai_key: str):
        # Initialize Pinecone
        pinecone.init(api_key=pinecone_key, environment="us-west1-gcp")

        # Create embeddings
        self.embeddings = OpenAIEmbeddings(openai_api_key=openai_key)

        # Connect to vector store
        self.vector_store = Pinecone.from_existing_index(
            index_name="knowledge-base",
            embedding=self.embeddings
        )

        # Initialize LLM
        self.llm = OpenAI(temperature=0, openai_api_key=openai_key)

        # Create retrieval chain
        self.qa_chain = RetrievalQA.from_chain_type(
            llm=self.llm,
            chain_type="stuff",
            retriever=self.vector_store.as_retriever(search_kwargs={"k": 5}),
            return_source_documents=True
        )

    def query(self, question: str) -> dict:
        """Process question with RAG pipeline"""
        result = self.qa_chain({"query": question})

        return {
            "answer": result["result"],
            "sources": [doc.page_content[:200] + "..."
                       for doc in result["source_documents"]],
            "confidence": self.calculate_confidence(result)
        }

    def calculate_confidence(self, result) -> float:
        # Implement confidence scoring based on source relevance
        return 0.95  # Simplified for demo

# Usage: Handle 50,000+ queries monthly like DoorDash
rag_system = ProductionRAGSystem(pinecone_key, openai_key)
response = rag_system.query("How do I implement distributed caching?")

What You'll Build

📚

Week 9-10: Document Q&A System

Process 1000+ documents with 95% accuracy. Handle PDF, Word, and web content like a production system.

🔍

Week 11-12: Semantic Search Engine

Build Google-like search with semantic understanding. Sub-100ms response times on billion-vector datasets.

🤖

Week 13-14: Multi-Agent System

Agents that research, analyze, and report. Automate complex workflows like Accenture's enterprise solutions.

📊

Week 15-16: Analytics Dashboard

Real-time monitoring, cost tracking, and performance optimization. Production-ready observability.

Integrating AI