Vijendra Kotian

Fullstack Developer with Javascript,Python,Java RESTful architectures.

Building a RAG system from scratch

You will know the tools you need to work on this topic

Setup requirements

You have a website for selling a product. You want to add RAG to answer visitor questions about the product.

Workshop

Use images and videos to display based on the question asked.

for the next work

Learn the latest AI Search skills with the authors of "AI-Powered Search"

AI Search Expert | Founder | Author

Lucidworks

Presearch

Optimized AI Conference

CareerBuilder

Searchkernel

Trey Grainger

Founder, Searchkernel. Author, "AI-Powered Search". 18+ years leading search.

Principal Search Engineer | Author

Shopify.com

OpenSource Connections

Reddit

Wikipedia

Yelp

Doug Turnbull

Principal Search Engineer, Author of "AI-Powered Search" & "Relevant Search".

July 2026

Nov 2026

AI Search is among of the most in-demand AI Skills. AI systems need RAG, and implementing great retrieval can often have a bigger impact than prompt engineering for improving your AI and Agentic systems. Most search systems barely scratch the surface of what's possible. While others struggle with irrelevant results and frustrated users, you'll master the cutting-edge techniques that power the world's most successful search experiences—from Reddit and Spotify to the Fortune 500.Why This Course ExistsThe Problem: Many traditional search systems are stuck in the 2010s. But modern AI courses hyper focus on text embeddings and ignore many of the fundamentals of information retrieval. The result? Engineers who can't bridge the gap between amazing demos and production systems that actually work.Our Solution: Stop downloading random embeddings from Hugging Face or OpenAI and hoping for the best. Learn to systematically analyze your users' actual intent, integrate domain context, and build search that truly understands what people (and agents) need—not just what they type. This course helps you build cutting-edge AI Search systems that actually work.Ready to level-up your AI Search skills?

Level up your Agentic Search & Retrieval skills with leading AI Search experts

Optimize your Agentic Search and Retrieval Augmented Generation (RAG) systems

Go beyond the query to understand intent

Fill in holes in your modern information retrieval knowledge

Improve relevance with crowdsourced ranking models built from click-stream data

Optimize AI-Powered Search Models for Production

Build ranking features & automate judgements with click models & Active Learning

Examples will be taught in Python, it's a good idea to have basic Python familiarity under your belt

Basic Python Programming

For those new to search/retrieval, we provide an optional intro session covering core matching & ranking concepts (BM25, vector spaces, etc)

(Included Complimentary with the Course): Search Fundamentals

AI Engineers optimizing search ranking to measurably improve user search satisfaction

Search Practitioners that want to learn to systematically improve search relevance

Technology leaders hoping to plan search experiments to maximize clicks and conversions

Create your own production-ready AI application in 6 weeks

Teaching AI and Data to 100k+ students 

DataTalksClub

Manning Publications

OLX Group

Alexey Grigorev 

15 years of experience. Teaching AI and Data to 100k+ students

Cohort 3

Cohort 4

This course takes you from core concepts to production-grade AI systems through hands-on, project-focused modules.<ol><li>LLMs &amp; RAG Learn Large Language Models and Retrieval-Augmented Generation. Build conversational agents using the OpenAI SDK and create data-processing pipelines. Outcome: A RAG pipeline with real data.</li><li>Agentic Flows Add agentic behavior with function calling, using libraries like PydanticAI, and Agents SDK. Expose tools via MCP. Outcome: A capable, tool-using agents.</li><li>Testing &amp; Evaluation Improve through testing and offline evaluation. Use LLMs as judges to compare approaches. Learn tools like Evidently and LangWatch. Outcome: A thoroughly tested and evaluated assistant.</li><li>Monitoring Use Grafana, Pydantic Logfire and OpenTelemetry for observability and safety. Outcome: Real-time monitoring.</li><li>Use Cases Create two agents: a website generator and a code reviewer. Learn about other use cases.</li><li>Capstone Build an end-to-end AI application with your data. Outcome: A portfolio-ready project.</li></ol>Hackathon: Collaborate on real-world problems.By the end, you will: build, evaluate and monitor a smart assistant; create deep research and coding agents; have a polished portfolio project

Build your own Agentic AI Assistant

🛠️ Build a Fully Functional AI Assistant from Scratch

🤖 Add Agentic Behavior to Your AI Systems

🔧 Use Testing and Evaluation to Improve Prompts and Results

📈 Monitor Your Application 

🔧 Build 8+ Projects 

🎓 Ship a Capstone AI Project — Start to Finish

Coding

We will rely on these tools when building the assistant

Python, Git, Docker, command line

We will use OpenAI for building the AI agent

OpenAI key or alternative

Data Scientists and ML Engineers proficient at coding who want to integrate AI into their projects

Software Engineers curious about LLMs who want to build AI applications

AI Enthusiasts who are stuck at the tutorial phase and want to create something end-to-end

Build with the LLM APIs, RAG, LangGraph, MCP, A2A, evals, and agent memory, using Claude Code and Cursor, across 4 hands-on weeks. 

Cohort 10 [ongoing]

Cohort 13 [closed]

Cohort 14

Cohort 16

Cohort 15

PhD in ML | Google AI Accelerator Alum

Google

Amazon Web Services

OpenAI

NVIDIA

Dr. Aki Wijesundara

AI Founder | Educator | Google AI Accelerator Alum

Exited AI Founder | Founder, TAI Labs

Boston Consulting Group (BCG)

McKinsey & Company

Manu Jayawardana

AI Advisor | Founder, TAI Labs

Cohort 12 [closed]

Cohort 17

We revamp the syllabus every two weeks, so you're always covering the latest in AI development, from loop engineering to graph engineering.We're the highest-rated AI engineering bootcamp, and most recently the fastest-growing.Our teaching philosophy is simple: you learn by building. With coding agents like Claude Code, Codex, and Cursor, anyone can become a builder, and therefore an AI Engineer.What you'll master and build:<ul><li>Production-grade AI endpoints with guardrails</li><li>Embeddings, vector databases, and RAG</li><li>Agentic loops, multi-agent architectures, MCP, and A2A</li><li>AI evals using the TRACE framework</li><li>Agent memory: long-term, short-term, and skills</li></ul>Our students come from MAANG companies, and <a target="_blank" rel="noopener noreferrer nofollow" href="https://tailabs.ai/#:~:text=career%20change.%E2%80%9D-,Zainab%20Ahmed%2DSafeer,-Google%20%C2%B7%20Chicago">some have landed roles at MAANG straight after finishing the bootcamp.</a>Want to see how we teach first? Watch our free recorded mini-series, <a target="_blank" rel="noopener noreferrer nofollow" class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" href="https://theaiinternship.com/courses/think-like-an-ai-engineer/">Think Like an AI Engineer</a>.A limited number of need-based scholarships are available for applicants facing real financial hardship. <a target="_blank" rel="noopener noreferrer nofollow" class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" href="https://theaiinternship.com/scholarship">See if you qualify.</a>

Early Bird Discount ends in 24 hours; Grab your seat now!

Engineer against a model, not just prompt it

Build RAG you can measure and actually trust

Build AI agents from the loop up

Run evals the serious teams run, with TRACE

Give your agent memory that compounds

Ship a portfolio that proves you can build

The one thing you really need. If you want to understand how AI systems work and build real ones, you are ready to start.

Curiosity and a drive to build

You do not need to be a strong coder. We build with coding agents, so you skill up as you build. Comfort with basic logic helps.

Some code, or willingness to pick it up

We use Claude Code and Cursor. They let you write and understand real code fast, even when it is new to you. Setup is covered in pre-course.

A coding agent to build with

The engineer moving into AI. Software, data, or ML. You want to build LLM systems and agents in production, not just talk about them.

The PM who wants to really understand AI. Done nodding along. Grasp RAG, agents, and evals deeply enough to lead and challenge a build.

The career mover. Targeting AI engineering or FDE roles. You need shipped systems and real understanding, not another certificate.

Why Building a RAG System as a PM Matters

AI Product Leader & Builder

Oracle

S&P Global

Weill Cornell Medicine

Scaled Agile

Priyanka Shetty

Principal PM (Oracle, S&P Global, Weill Cornell) | AI Strategist & Founder

Cohort 1

Every PM is being asked to "add AI" to their roadmap. Almost none of them can tell you the difference between a retrieval problem and a generation problem, or explain why their RAG demo works in the sandbox and falls apart in production.That gap isn't a technical skills gap — it's a product judgment gap. Engineering can build whatever architecture you ask for. The job of the PM is knowing what to ask for, what tradeoffs that choice creates, and how to evaluate whether it's actually working. That's a scoping and judgment problem, and it's learnable the same way any other product skill is: by building it, breaking it, and fixing it yourself.This workshop exists because most "learn AI" content for PMs stays at the level of concepts and vocabulary. You'll leave able to defend an architecture decision in a room full of engineers — because you'll have made that decision yourself, watched it fail, and rebuilt it.

This is a build day, not a lecture day. By the end of the workshop, you won't just understand RAG conceptually — you'll have built one.

Scope a RAG System like a PM

Know when RAG is the wrong call

Build a working RAG pipeline yourself

Defend your architecture in the room

PMs who keep hearing "add AI" on their roadmap and need to scope it credibly — not just nod along in meetings.

Product leaders who review AI architecture decisions and want enough technical grounding to push back when it matters

PMs building their own AI skill set for their next role — AI Builder, AI PM, or a pivot into AI product work.

Building a RAG system from scratch

What you'll learn

Why this topic matters

You'll learn from

Vijendra Kotian