Doug Turnbull

October 2025

Nov 2025

Cohort 1

July 2025

Principal AI Engineer in Search

Cheat at Search with LLMs

Software Engineer

Consulting Architect

Machine Learning Engineer

Staff Data Scientist

Software Engineering specialist 

AI/ML Engineer

Senior Consultant

Data Scientist

Software Engineering Manager

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Search startups eager to get a fast start with a modern approach to search, sidestepping old, outdated practices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

AI Chat Startups - Is the bubble about to pop?

Name

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

What should you actually build with AI?

WTF is an NDCG!? - Search Relevance Evaluation

Ex Reddit, Ex Shopify 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

Could your search be better without vectors? BM25 + friends

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

Semantic search without the vectors

Navigating your tech career in the age of layoffs and AI

Team Lead at Shopify, Reddit, and AI Startups

Brian Pedersen

Principal, "The Search Bar"

What tools and skills should you acquire to thrive in with ubiquitious AI and looming layoffs?

How can I future-proof my career?

How to match up what makes you happy with the needs of the job market

What does fulfillment look like?

What skills do hiring managers focus on increasingly? What can help you standout on your team, or in the next interview?

What do hiring managers want?

Hybrid search live coded from scratch

Led Search at Shopify, Reddit

Ben Trent

Sr. Principal Engineer Elastic

How a vector search index, like Elasticsearch's, implements filtering using algorithms to combine with lexical retrieval

Internals of hybrid search

As you watch an expert live-code, see where the algorithm falls apart as a real-life expert makes a mistake

See where hybrid search goes wrong

Dense vectors provide specific constraints on retrieval solutions - learn what these are, how they can go wrong

Deepen your vector search knowledge

CTO's Playbook for Faster Agentic RAG

Nirant Kasiwal

Creator FastEmbed

Led Search at Reddit, Shopify, Wikipedia

know exactly when to swap OpenAI for FastEmbed and improve both recall and latency.

Latency vs Recall Cheat-Sheet

live-coded: BM25 + vector ➜ tool-calling agent

Hybrid-to-Agentic Upgrade Path

7-point checklist that spots 90 % of real-world retrieval failures before users do.

Stealable RAG Audit Framework

Build a vector database from scratch (part two)

Eric Pugh

Cofounder OpenSource Connections

Learn the most common graph-based vector search algorithm used in Elasticsearch, Weaviate, QDrant, Pinecone, etc

Doug Turnbull

Doug Turnbull

Courses

Lightning Lessons

Reviews