Shubham Saboo

Semantic Search with Low-Code

Tenstorrent

Jina AI

Arcesium

AI Author, Product Manager at Tenstorrent, Founder of Unwind AI

Create a powerful RAG pipeline for semantic search with a few lines of Python code

Build RAG pipeline for semantic search

Ingest data from multiple sources into a vector database with minimal Python code

Integrate data from multiple sources

Setup and deploy a semantic search pipeline end-to-end using Embedchain and Streamlit

Deploy your application in a few clicks

Master Advanced Techniques for Building and Optimizing Agentic RAG Systems and Multi-Agent Workflows — Designed for Builders

2026-05

Founder | Ex-Google | Prof UCLA & UMN

Google

Walmart

University of Minnesota

Stanford Continuing Studies

UCLA

Hamza Farooq

Founder | Ex-Google |
Adjunct UCLA & UMN, SCU | Venture Partner

Staff AI/ML Engineer (DX) @ Together AI 

Coursera

Together AI

University of Toronto

Weaviate AI Database

DeepLearning.AI

Zain Hasan

Staff AI/ML Engineer (DX) @ Together AI | AI Educator & Researcher | Founder

2026-01

<h1>Master the full agent stack from first principles to production with RAG, voice, multi-agent systems, &amp; inference</h1>6 live sessions · 6 office hours · 1 demo day · 20+ hours<h3>What You’ll Build</h3>⚡ Agent Harness Design: Build a observable perceive–think–act loop in Python with structured tracing, evolving from simple LLM calls to full agent systems with tools, reflection, and planning⚡ Agentic RAG Systems: Create advanced RAG pipelines with routing, multi-hop reasoning, Knowledge Graph, and semantic caching to improve accuracy and efficiency⚡ Real-Time Voice Agents: Engineer a streaming STT → LLM → TTS pipeline with turn-taking, interruptions, and sub-second latency across modern voice platforms⚡ Multi-Agent Orchestration: Design coordinated agent systems using MCP and A2A with patterns like hierarchical, debate, and orchestrator-worker workflows⚡ Guardrails &amp; Evaluation: Build reliable systems with safety guardrails and trajectory-based evaluation using LLM-as-judge and validated benchmark tasks⚡ Production Deployment: Ship multi-agent systems with Google ADK, MCP, A2A, Llama Guard, and GCP monitoring<h3>💻 Prerequisites: Python</h3>👉 This bootcamp is for engineers who ship real agent systems

Build Production-Ready Agentic RAG, Voice & Multi-Agent Systems 

Build and Debug Production Agent Harness

Deploy and Optimize LLM Inference

Ship Agentic RAG with Graphs and Semantic Cache

Build Voice Agents That Feel Alive

Operate Multi-Agent Systems with Real Coordination

Evaluate, Guardrail, and Operate with Confidence

Machine Learning Engineer exploring different techniques to scale LLM solutions

Researcher, who would like to delve in to various aspects of open-source LLMs

Software Engineer, looking to learn how to integrate AI into their products

Rethink search to put tireless agents at work for human users

June 2026

http://softwaredoug.com

Doug Turnbull

Snuggie and Search Enthusiast

Feb 2026

Oct 2026

Keyword search + an agent - is that all we need now? The agentic world challenges traditional ways of building search. Traditionally we've built thick, complex search APIs - query understanding, rerankers, learning to rank, vector databases. Yet an agent can compete in deep research benchmarks with the dumbest of search tools.This course pushes you to rethink search. We teach:<ul><li>Replacing search with agents - A search agent learns about retrieval systems, tailoring searches, achieving competitive results</li><li>Guiding agents, not algorithms - Search developers won't train algorithms. They'll build harnesses</li><li>Steering with an LLM judge - Integrate human and clickstream feedback to guide the agent's judgment towards what users want</li><li>Autoresearching to smarter search - Use + build a coding agent that optimizes retrieval code like an ML model.</li></ul>Come learn alongside search industry expert <a target="_blank" rel="noopener noreferrer nofollow" href="http://softwaredoug.com">Doug Turnbull</a> as we unpack how agents will rewrite the search industry.

The future of search is agentic. Are you ready?

What makes a great search tool to an agent?

Use LLMs to help agents structure, organize, and find content

Agentic results without the cost

Orienting agentic search towards the human, not the machi

We will build on top of existing, core search knowledge

Lexical Search basics (do you know what BM25 is)

We will use common search datasets that use metrics like NDCG, etc to measure search

Basic search evaluation (judgments + metrics)

Vector search will be one tool in our toolkit we'll apply to the agentic tools that we build

Vector search basics (do you know what an embedding is?)

Search technologists that want to learn how to ensure RAG and agentic search win

Startups eager to get a fast start with a modern approach to search, sidestepping old, outdated practices

Data scientists that want to consider how agentic / LLM / RAG based search can be oriented towards user feedback and business outcomes

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

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

March-2026

July 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

Make embedding retrieval an asset, not a headache

Led Search + ML at Reddit and Shopify

Apple

Amazon Web Services

Search at Reddit, Shopify, Wikipedia

August 2026

In modern search, embeddings answer questions. They go beyond simple keyword matching to find semantically similar answers. Increasingly: RAG, recommendations, and traditional search need zero-in on what's relevant. In this class we'll live-code core data structures that live behind search systems like Pinecone, Weaviate, Turbopuffer, QDrant, Vespa, Elasticsearch etc etc etc.By building your own vector database, you'll be better equiped to work with production vector search systems. You'll have first-hand experience with the knobs to turn to improve performance and develop a robust hybrid + vector search system

Infrastructure teams - anyone tasked to squeeze the most performance out of a high-scale retrieval system

Search developers - anyone that needs to build relevant, fast vector search for RAG or agentic search applications

AI engineers - need to find that relevant context? Anyone who needs to find context to answer questions from AI

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Engineers and engineering leaders who build, deploy, and scale software systems.

Data scientists who apply statistical and ML techniques to extract insights from data.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building retrieval-augmented generation and search systems — vector databases, embeddings, ranking, and retrieval pipelines.

Semantic Search with Low-Code

What you'll learn

Why this topic matters

You'll learn from

Shubham Saboo