Production-Focused Program

Your RAG demo works. Production RAG is a different system — build it end to end.

Latency budgets. Caching layers. Retrieval trade-offs. Guardrails. Observability. Deployment.

Where you are: you can wire a retriever to an LLM and get good answers on your laptop. The gap: latency budgets, cache invalidation, multi-vector retrieval, guardrails and trace-level observability — the parts that only show up under real traffic. Where this takes you: a RAG system you can deploy, instrument, cost out and defend in a design review. The path: two full builds, from data ingestion to a FastAPI service. Systems ship. Demos don't.

Start building the production version →See if this closes your gap ↓

✓ Lifetime access  ·  ✓ Source code included  ·  ✓ 2 production projects

Free Demo: See how AI agent demos become production services →
What You'll Build
  • ✓ End-to-end production RAG pipeline with Redis caching
  • ✓ Multi-vector retrieval with intelligent chunking
  • ✓ Guardrails + observability instrumentation
  • ✓ FastAPI deployment with full orchestration
  • ✓ Supabase-backed production project
25
Lectures
5h
Total Content
2
Projects
25
Lectures
5h
Total Content
2
Production Projects
100K+
Engineers Mentored

Where Demo RAG Breaks Under Real Traffic

You already know the components. What is missing is how they behave once concurrency, cost and stale data arrive — and who on the team can explain why.

🐢

Latency you can't explain

You built a RAG pipeline but have no caching strategy, no async retrieval, and no way to diagnose why queries take 4 seconds in production.

🔍

Retrieval that doesn't scale

Single-vector, naive chunking fails on complex queries. Multi-hop retrieval, reranking, and metadata filtering are production requirements — not advanced topics.

🔥

No safety, no visibility

Without guardrails and observability, you can't catch hallucinations, cost spikes, or silent failures. Production systems need these from day one.

The Path From Demo to Deployment

Five sections, in the order the system gets built · 31 lectures · 8 hours — retrieval, caching, guardrails, observability, deployment

01

Introduction & Source Code

Environment setup + project foundation
1 lecture

Section Goal: Get your environment configured, project files downloaded, and understand the overall architecture before writing any code.

  • Source Code & Project Resources
🛠️ What You Get: Full source code repository and all project resources to follow along with every implementation in the course.
02

Introducing LangChain

The framework you'll use to compose the production RAG pipeline
6 lectures~1 hour

Section Goal: Get fluent with LangChain — LLM calls, chains, prompts, templates, and structured JSON output. These are the composable building blocks you will apply to every RAG component in the sections ahead, from retrieval orchestration through to production guardrails.

  • Introduction to LangChain
  • Hello LangChain — First LLM Call
  • Chains & Prompts in LangChain
  • Working with Templates — Hands On
  • Making Templates Dynamic
  • Outputformat to JSON — Hands On
🛠️ What You Get: Hands-on LangChain fluency across chains, prompt templates, and structured JSON output — the pattern language you will apply to every RAG component that follows.
03

RAG Foundations

Indexing, chunking, retrieval — done right
7 lectures

Section Goal: Build a solid foundation in RAG architecture — from understanding how indexing works to implementing multi-vector retrieval strategies that hold up under real query patterns.

  • Introduction to RAG
  • Understand Indexing in RAG
  • Prep for Indexing — Chunking Strategies
  • Loading Data to Vector Database
  • Recording Transactions & Multi Vector Retrieval
  • Implementing Multi Vector Retrieval
  • RRR — RAG (Retrieve, Rerank, Respond)
🎯 Why This Matters: Chunking strategy and retrieval design are the two most common interview probes on RAG architecture. Getting these right is the difference between a prototype and a production system.
04

Production RAG System Architecture

The core of the program — 16 lectures end-to-end
16 lectures2hr 52min

Section Goal: Build a complete production RAG pipeline from scratch — ingestion, caching, vector store, orchestration, guardrails, observability, and FastAPI deployment. Every layer you'd need to ship this to real users.

  • Introduction to GenAI System Design 7min
  • Anatomy of a GenAI Pipeline 10min
  • Building Blocks of a Generative AI Pipeline 11min
  • Getting Project Files Ready 17min
  • Redis Cache Store — Labs Part 1 9min
  • Redis Cache Store — Labs Part 2 6min
  • Vector Store — Labs 11min
  • Loading Data to Vector Database 15min
  • Test Vector Retrieval 5min
  • Prompt Router 8min
  • LLM Call 7min
  • Post Processing the Output 5min
  • Orchestration of RAG Pipeline 15min
  • Guardrails Implementation 11min
  • Observability 18min
  • FastAPI Deployment 16min
🎯 Why Interviewers Care: Anyone can build a RAG demo. Can you explain how you'd cache queries, route prompts, implement guardrails, trace failures, and deploy it to prod? This section gives you that end-to-end story.
05

Project 2 — RAG with Supabase

Second production project with a different stack
1 lecture

Section Goal: Build a complete second production RAG system using Supabase as the backend — giving you experience with a different architecture pattern and a second portfolio project to demonstrate.

  • RAG with Supabase — Full Implementation
🛠️ What You Get: A second working project with full source code, demonstrating how RAG architecture adapts across different backend infrastructure choices.

The Stack You'll Make Decisions In

Every choice here costs you something — latency, spend, or operational load. You will know which one you are paying, and why.

Vector Storage
Supabase pgvector · Redis · Multi-vector indexes
Retrieval
Multi-vector retrieval · Intelligent chunking · Reranking (RRR)
Caching
Redis cache store · Query-level caching · Cache invalidation patterns
Orchestration
Pipeline orchestration · Prompt routing · LLM call management
Production Safety
Guardrails implementation · Output post-processing · Safety layers
Deployment & Observability
FastAPI · Observability instrumentation · Trace-level monitoring

What You'll Be Able To Do

Two complete builds, plus the reasoning behind each decision — so you can reproduce the pattern at work, not only inside the course.

🏗️

2 Production Projects

₹12,000

Complete implementations from data ingestion to FastAPI deployment — with source code

⚡

Redis Caching Layer

₹6,000

Full labs covering cache store setup, query caching, and invalidation patterns

🔍

Multi-Vector Retrieval

₹8,000

Advanced retrieval strategies with RRR (Retrieve, Rerank, Respond) implementation

🛡️

Guardrails + Observability

₹7,000

Production safety and monitoring frameworks you can drop into any pipeline

🚀

FastAPI Deployment Blueprint

₹5,000

Full deployment walkthrough with production-ready API patterns

♾️

Lifetime Access

∞

All materials, source code, and future updates — yours forever

Who This Is Built For

Written for engineers who already ship software, and now need the AI systems layer on top of the experience they have.

✔

Software Engineers building AI-powered products

✔

ML Engineers adding production RAG to their stack

✔

Backend Engineers transitioning into AI engineering

✔

Engineers preparing for senior AI system design interviews

This program is NOT for you if:

  • You're a beginner looking for a "What is RAG?" introduction
  • You have no Python or AI fundamentals
  • You want theory without implementation

Your Next Step

One-time investment. Two production projects. Lifetime access.

Production RAG Program
Production RAG System Design
From data ingestion to FastAPI deployment
  • ✓ 31 lectures · 8 hours of hands-on content
  • ✓ 2 complete production-style projects
  • ✓ Full source code repository
  • ✓ Redis caching, multi-vector retrieval, guardrails, observability
  • ✓ FastAPI deployment walkthrough
  • ✓ Supabase-backed second project
  • ✓ Lifetime access with all future updates
Start the production build →
⚡ Instant access the moment you enroll — start building today.

What Engineers Took Back To Work

★★★★★
"After building my first production pipeline here, I walked into system design interviews with real examples — caching strategy, retrieval trade-offs, observability setup. Got the offer."
Ravi K.
ML Engineer → Senior AI Engineer, Bengaluru
★★★★★
"The focus on production constraints completely changed how I think about RAG. Redis caching and guardrails are not afterthoughts here — they're baked in from the start."
Sneha P.
Software Engineer, AI Platform Team, Pune
★★★★★
"Finally a program that teaches architectural decisions, not just code. The observability section and FastAPI deployment alone were worth it — I shipped this pattern at work within a week."
Aditya M.
Backend Engineer → AI Engineering, Hyderabad
Nachiketh Murthy

Nachiketh Murthy

AI Architect · Agentic AI Mentor

Nachiketh Murthy teaches at Manifold AI Learning. He works with experienced technology professionals — engineers, architects, data and cloud specialists — who want to move past AI demos and build production-ready systems without discarding the experience they already have.

Every program he designs starts from a production decision: what breaks, what it costs, and how you defend the choice in a room full of engineers.

100K+
Professionals Mentored
3+
Cloud Platforms
1M
AI Leaders Goal

What Happens Once You Are In

Four steps between here and your first commit.

💳
Complete Payment
One-time, secure checkout via Razorpay
📧
Instant Access Email
Login credentials arrive immediately
💻
Clone the Repo
Get source code and set up your environment
🚀
Start Building
Work through 31 lectures at your own pace
Next Step After This Program

Where this takes you next

One retrieval system in production is the first step. The next gap is many of them coordinating, and someone accountable for the bill. The Agentic AI Enterprise Mastery Bootcamp covers multi-agent architecture, evaluation loops, cost governance and enterprise rollout.

See what enterprise scale demands →

Worried your foundations are rusty? Nothing is required first — the Agentic AI Enterprise Mastery Bootcamp includes Python Foundations for Agentic AI and LangChain Agentic AI Foundations free, so you walk into week one already fluent in the stack the cohort builds on. Next cohort starts the last week of September 2026.

Frequently Asked Questions

Is this beginner-friendly?expand_less
No. Basic Python and AI familiarity is recommended. This program assumes you know what RAG is and focuses on building production-capable systems — not introducing the concept.
Is this hands-on?expand_less
Yes. Two production-style builds with complete implementations — from data ingestion through Redis caching, vector retrieval, guardrails, and FastAPI deployment. Full source code included.
What tech stack does this use?expand_less
Redis for caching, Supabase pgvector for the second project, FastAPI for deployment, and standard Python tooling for orchestration, guardrails, and observability. Real production tools, not toy wrappers.
Does this include reranking or cost attribution?expand_less
The RRR pattern (Retrieve, Rerank, Respond) is covered in Section 2. Advanced cost attribution and optimization are covered deeper in the Agentic AI Enterprise Mastery Bootcamp.
Do I get lifetime access?expand_less
Yes. All materials, source code, and any future additions are included with a one-time payment. No subscription required.
What's the refund policy?expand_less
Because access and materials are delivered when you enroll, refunds are limited once your account is activated. Not sure this is the right fit? Email support@manifoldailearning.in before you enroll—we're happy to help. See our Refund Policy for details.
Does this program come with a job or placement guarantee?expand_less
No. Manifold AI Learning does not offer or imply any job, placement, hiring, salary, or income guarantee — for this program or any other. This is a premium learning program designed to strengthen your engineering judgment, system thinking, and ability to explain decisions like a senior engineer. Outcomes from there depend entirely on your own effort, applications, and performance.
🏗️ Production RAG System Design  ·  31 Lectures · 8 Hours · 2 Projects
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