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Cheat at Search with LLMs

  • Intermediate

This course offers a practical approach for search teams to adopt LLMs to improve search. Learn how to drive relevant, profitable search results quickly and efficiently, without needing a PhD.

  • LLM integration
  • Search engine optimization
  • Query understanding
  • Content understanding
  • Search relevance

Overview

In this course, you'll learn how to enhance your search engine's relevance using Large Language Models (LLMs). The course covers query and document understanding, deploying LLMs to production, and making unstructured search structured. You'll gain insights into modern search techniques, helping your team achieve better outcomes faster.

  • Layers 1 Streamline Icon: https://streamlinehq.com
    English
    course language
  • Professional Certification
    upon course completion

Who is this course for?

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Eager to get a fast start with a modern approach to search, sidestepping old, outdated practices.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.

This course empowers search teams to enhance their engines using LLMs, achieving relevant results quickly. Ideal for search professionals and managers, it offers practical skills to improve search relevance and efficiency.

Pre-Requisites

1 / 3

  • Basic understanding of search engine technology

  • Familiarity with AI concepts

  • Interest in improving search relevance

What will you learn?

Week 1: Query corrections with LLMs
Learn how to correct queries using Large Language Models to improve search accuracy.
Week 2: Zero shot query understanding with LLMs
Explore zero shot query understanding techniques to enhance search relevance.
Week 2: Few shot query understanding
Understand few shot query understanding to improve search outcomes.
Week 2: Content understanding
Gain insights into content understanding to make search more relevant.
Week 3: Modeling similarity
Learn how to model similarity to enhance search results.
Week 3: Two tower embedding models
Explore two tower embedding models for better search relevance.
Week 3: Hybrid Search
Understand hybrid search techniques combining lexical and vector retrieval.
Week 4: LLM as a judge
Learn how to use LLMs as a judge to improve search ranking.
Week 4: LLM as a judge/pairwise ranker
Explore using LLMs as a pairwise ranker for better search outcomes.

Meet your instructor

  • Doug Turnbull

    Principal AI Engineer in Search, Daydream

    Doug Turnbull is an expert in search technology and relevance engineering, currently serving as Principal Engineer at Daydream. He has led machine-learning-driven search initiatives at Reddit and Shopify, and co-authored the influential book Relevant Search.

New cohorts coming soon