Doug Turnbull

Nov 2025

Cohort 1

July 2025

October 2025

Feb 2026

Rethink search to put tireless agents at work for human users

http://softwaredoug.com

Led Search Reddit + Shopify. Wrote Relevant Search + AI Powered Search

Cheat at Search with Agents

Product Manager

Principal Engineer

Indie Consultant

Software Engineer

Principal recommender engineer 

Search Consultant

Generative AI Engineer

Software Engineer Search

Engineering Manager

Humans increasingly search via chat, not search bars. Agentic search goes beyond RAG to incorporate agentic reasoning into the search process. Agents iterate, refine, and learn to use search tools in away monolithic RAG fails to.Come to this class to:<ul><li>Leverage agentic reasoning to go beyond RAG - Traditional RAG can't reason. An agent learns about retrieval systems and tailors its searches for more accurate results. Go beyond naive RAG towards agentic search</li><li>Craft effective retrieval - Focus an agent's reasoning on the user's task, not "search", with task-targeted search tools. Tools built for agents; not for humans.</li><li>Go beyond LLM as a judge - Integrate human and clickstream feedback to guide the agent's judgment towards what users want</li><li>Improve keyword search - Take lessons learned through agentic search back to traditional "search bar" search, simplifying query and content understanding</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 change the search industry.This class is a fun and experimental course, focused on play and experimentation. It's part of my quest to find new, practical frontiers in Information Retrieval. I hope you can join me!

Agents are the new search engine. Go beyond RAG and keywords.

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

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

Course syllabus

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

Mondays

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

Weekly projects

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

Thursdays

Course schedule

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

Who is this course for

What you’ll get out of this course

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

Principal AI Engineer in Search

Nick Zadrozny

Founder + CEO Bonsai

Building Deep Research Systems

Andrei Lopatenko

Search at Zillow, Google, eBay, Apple

Agentic Search Consultant

How retrieval augmentation and semantic grounding expand LLM capabilities for knowledge discovery.

Pushing beyond the LLMs knowledge

The design principles behind multi-hop reasoning and deep exploration architectures.

Deep Research Design Principles

Evaluation methods and frameworks shaping next-generation research and information systems.

The next generation of search eval

Beyond chunks - how to actually data model for RAG

Radu Gheorghe

Software Engineer, Vespa.ai

Trade-offs between large documents with chunk arrays and chunk documents with denormalized metadata

Why preferring large documents works best for RAG

Combining chunk and document-level features into a single searchable entity

The ideal data model for RAG retrieval

Using rank profiles to compute chunk relevance within each document and return top N chunks for each

How to surface the most relevant chunks

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

AI Chat Startups - Is the bubble about to pop?

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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