The Complete RAG System

Mother of AI Project, Phase 1: Week 5

Shantanu Ladhwe and Shirin Khosravi Jam

Sep 05, 2025

∙ Paid

Hey there 👋,

Welcome to lesson five of "The Mother of AI" - Zero to RAG series!

Quick recap:

A strong RAG system is a chain: infra → data → search → generation.

Weeks 1–4 gave us solid infrastructure, live data pipeline, BM25 keyword search, and hybrid retrieval with semantic understanding. Now we complete the loop with LLM integration that delivers production-grade performance.

Week 1: The Infrastructure That Powers RAG Systems
Week 2: Bringing Your RAG System to Life - The Data Pipeline
Week 3: The Search Foundation Every RAG System Needs
Week 4: Chunking Strategies and Hybrid RAG System

Most teams integrate LLMs and call it done. We won't. We'll implement smart prompt optimization, streaming responses for better UX, and a production-ready interface that actually works.

Important: If interested in Code walkthrough and explanation videos (PAID)

Check below:

✅ Live walkthrough of that week’s code
✅ Deeper insights into design tradeoffs, infra, and architecture
✅ Debugging support on your implementation
✅ How to go beyond and deploy these solutions in production

Course → https://jamwithai.dev/

Use Coupon code - JMJCAWJ64E to get 30% discount.

This week's goals

Integrate Ollama for local LLM inference with llama3.2 models
Optimize prompts with smart context reduction strategies
Implement streaming with Server-Sent Events for real-time responses
Build Gradio interface for interactive RAG system testing
Create production API with two focused endpoints for different use cases

Deliverables

Ollama service integration with automatic model management and health checks
Optimized prompt templates with minimal context for efficiency
Dual API design supporting both complete responses and streaming
Gradio web interface with real-time streaming and source citations
Production configuration with environment-based settings and error handling

Big picture: We took the hybrid search from Week 4 and connected it to local LLM inference, creating a complete RAG pipeline.

The key insight: removing redundant metadata and limiting response length delivers cleaner prompts without sacrificing answer quality.

The architecture now includes a complete generation layer: hybrid search retrieves relevant chunks, minimal prompts preserve context window, and Ollama generates focused answers with source citations.

What we built (high level)

Complete RAG system with LLM generation layer (Ollama), hybrid retrieval pipeline, and Gradio interface