Doug Turnbull

October 2025

Nov 2025

Cohort 1

July 2025

Learn to build LLM based query + content understanding in search

Principal AI Engineer in Search

Cheat at Search with LLMs

Engineering Manager

Software Engineer

Consulting Architect

Machine Learning Engineer

Staff Data Scientist

Software Engineering specialist 

AI/ML Engineer

Senior Consultant

Achievable, Relevant Search without the PhD Great query and document understanding once took months of dedicated expertise to see results. With AI, you can see wins within weeks or days. Learn how to cut to the heart of your user's search problems, driving relevant, profitable search results.

Practical approach for search teams to adopt LLMs to improve search

Course syllabus

Mondays

Hands on projects making practical, LLM-driven search a reality

Weekly projects

Wednesdays

Fridays

Course schedule

Search teams eager to evolve that crufty-old search engine towards a modern approach

Search startups eager to get a fast start with a modern approach to search, sidestepping old, outdated practices

Team leads and managers excited to organize their work in a way aligned with modern approaches to Relevant Search

Who is this course for

What you’ll get out of this course

Help your understaffed search team see better outcomes faster with your search engine

Increase team velocity

Sidestep traditionally complex query and content enrichment with Large Language Models

Cheat at search with LLMs

Learn how there's no longer such a thing as "unstructured search". Turn any piece of query into a structured, well-formed query to your search backend

Make the unstructured, structured

Kick the theoretical to the curb. Deploying LLMs to production has gotten easier and easier. Smaller/local LLMs smarter. Do it all without a big OpenAI bill.

Make it a reality in production

We'll use Elasticsearch to build a hypercharged, AI-powered discovery system. Learn the core parts of building a high-quality search experience. From beginner to journeyman, all are welcome! In partnership with Bonsai (http://bonsai.io)

Practical Information Retrieval

Lecture time! Come and meet with Doug to discuss this weeks lesson. While there are slides, bring your questions!

Kick the tires with your own Elasticsearch cluster (courtesy of Bonsai) as you lean techniques used by cutting edge teams

Recap this weeks project, share with the group what you've learned, and recap the week's lesson

Thursdays

Search teams that want to leverage search engine capabilities to improve search relevance

AI teams that need a robust, scalable store to power RAG and other AI search applications

Anyone new to Elasticsearch hoping to get the most out of the most ubiquitous search engine on the planet

Practical tips to improve the search relevance in Elasticsearch applications

Improve search relevance

How to combine embeddings and traditional search to optimize search ranking for user feedback

Real-world hybrid search

Learn the tools Elasticsearch has to offer that many teams don't even realize can power cutting-edge applications

Discover Elasticsearch

Discover peers in the search space to network and grow with

Connect with the community

Nick Zadrozny

Founder + CEO Bonsai

Cheat at Search Essentials: Evaluation, NDCG, and pals

Ex-Reddit, Ex-Shopify. Author: AI Powered Search+Relevant Search

How the best search teams evaluate and measure search relevance

Basics of search relevance evaluation

When to ignore traditional search metrics and just trust your A/B tests. How to interpret offline metrics.

Where practice diverges from theory

Do you use clicks? Human labels? LLM as a judge? What are the pros/cons of these approaches?

Pros / cons of different types of labeled relevance data

The many shades of text search in vector retrieval

Evgeniya Sukhodolskaya

DevRel QDrant

Search consultant

How expectations from a traditional, keyword based search engine don't always translate

Mismatched Expectations from traditional to vector search

What are the differences between dense and sparse neural retrieval with text?

Dense vs Sparse Retrieval

QDrant's core "opinions" about text retrieval (text, vector, filters), and that leads to their implementation

How QDrant thinks about text retrieval

Cheat at Search Essentials: Vectors and Embeddings

Ex-Reddit, Ex-Shopify Search

What is a 'vector embedding' and why do they capture meaning? And how can that be used to build a search system?

Learn how embeddings implement 'semantic search'

Why do we need a vector database? And why are they different from traditional search engines? Should they be different?

Tackle the practical realities vector retrieval

Offer a concrete and concise explanation of how you will
help students understand and apply this lesson.


How are vector + lexical techniques combined?

Cheat at Search Essentials: BM25 + Lexical

Learn the baseline that rules them all (BM25) and how it works

Basics of Information Retrieval

How the simple decision of how to break up strings can make or break search

The importance of tokenization

How to achieve semantic search without vector search - and why you might prefer a lexical semantic approach

Cheat at semantic search without the vectors

How to choose the wrong vector database

Search Consulant, author AI Powered Search, Relevant Search

The tradeoffs different solutions make in scale, functionality, and performance

How vector databases differ from each other

Vector DBs are more than giant similarity machines -- learn what other features you need to build a robust application

Features you don't realize you need until too late

Learning to embrace decades of information retrieval when gathering context for your AI solutions

Beyond vector to hybrid and full text

Underrated: Elasticsearch for vector search + AI

Principal Consultant, SoftwareDoug LLC

Philipp Krenn

Head of DevRel at Elastic

How a search engine like Elasticsearch combines decades of Information Retrieval with vector retrieval innovations

Why you want a search engine, not just a vector DB

Learn about BBQ and other optimizations Elasticsearch has developed in vector retrieval

Elasticsearch's vector search innovations

How Elasticsearch's new features help target emerging use-cases like RAG

Elasticsearch for AI and RAG

Test Search on Any Website with Quepid and Javascript

Charlie Hull

Principal Consultant, The Search Juggler

Learn about Quepid, the leading open source tool for tuning relevance in Solr, Elasticsearch, and other search engines

What is Quepid - the search relevance workbench

How Charlie has developed a method for evaluating search for any website, directly through its web interface

How to connect Quepid to your site

Gathering human judgements of relevance lets you build offline test sets to tune search, augmented by LLM labels

From human to LLM labels

AI Chat Startups - Is the bubble about to pop?

Search Consultant

John Berryman

Founding Member, Github Copilot

The startup space is crowded with ChatGPT for X. For every X (legal, finance, commerce) there are dozens of startups!

Are we done with ChatGPT for X domain?

Beyond just repeating Chat for X patterns, how will AI transform the market? Are "agents" where its really at?

Where is AI headed next?

Where to invest your time and money into the highest leverage opportunities with AI given where AI is heading

Doug Turnbull

Doug Turnbull

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