Cheat at Search

Nov 2025

Cohort 1

October 2025

Feb 2026

July 2025

June 2026

Rethink search to put tireless agents at work for human users

http://softwaredoug.com

Doug Turnbull

Snuggie and Search Enthusiast

Cheat at Search with Agents

Senior Principal Data Scientist

Generative AI Engineer

Product Manager

full-stack developer

AI Engineer & Psychologist

Software Engineer

Consulting Architect

Student

Trainer, Lecturer, Author

Humans increasingly search via chat, not search bars. Go beyond naive RAG to guide agents and understand intentWe teach you how:<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 with 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 traditional search too - 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.We teach actual, practical solutions based on what works for real search teams. From query understanding with LLMs to late interaction

The no BS class on how search+RAG actually works

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Startups eager to get a fast start with a modern approach to search, sidestepping old, outdated practices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

Worked with 100+ teams on search quality

Shopify.com

Apple

Amazon

Reddit

Wikipedia

Search + Retrieval expert

Poor search destroys user experience. It frustrates users in e-commerce, legal, medical, and nearly every domain. Now with AI, we see agentic and RAG applications sink or swim on the accuracy of search results.In this workshop we uncover what's holding you back. What key problem areas prevent you from making progress on search? Why haven't you gotten to the next level?We'll stack-rank the highest impact changes you could make to your search.

Make a step change in search quality

Search teams - anyone feeling like their teams plateaued with search relevance and wants a way to get to the next level

Executives - leadership teams eager to strategize about where the direction to take their search strategy

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 retrieval 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

Cheat at Search Essentials: Embedding Retrieval

Led search teams at Reddit, Shopify, Wikipedia, and more

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 Retrieval

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

2026 will be the year of agentic search

Jo Kristian Bergum

Search + Retrieval Expert. CEO, Hornet.dev

Search + Snuggie Enthusiast

Harnesses, smarter models, and reasoning all upgraded agentic capabilities. 2026 that upgrade will be search.

Retrieval will be the major upgrade to agents in 2026

Is 'vector search' done? Why models like late interaction / ColBERT seem poised to reinvent how agents think + search

The power of late interaction, not just 'vector search'

How retrieval - not agentic reasoning - becomes the primary factor in deep research

What is "BrowseComp Plus" benchmark?

RAG is the what. Agentic Search is the how.

Retrieval Strategist - Ex-Reddit, Ex-Shopify

RAG fails because we've been taught a myopic view that misses out on the lessons of Information Retrieval

Good RAG isn't chunking + vector databases

We think RAG means "LLMs that search". That's a half step. Real progress comes from search agents that self correct

Agentic Search gets RAG unstuck

From reasoning to building a good harness - the craft of building a good RAG runs through agentic search - not vector DB

How RAG should actually be built - from first principles

Tuning HNSW parameters for filtered search

Radu Gheorghe

Software Engineer, Vespa.ai

Trey Grainger

Founder @ Searchkernel; Author, AI-Powered Search

We'll start with a short intro on how HNSW works and why filters are expensive and hurt recall.

Why filters make HNSW vector search slow

Pros and cons of post filters, ACORN-1, brute force kNN, overfetching, and adaptive beam search.

Parameters to improve performance/recall

How HNSWTuner+VespaNNParameterOptimizer allows you to change these knobs and see the impact of latency and recall

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