Staging environment

Search in the LLM Era for AI Engineers

6 Weeks

·

Cohort-based Course

Learn how to make a modern Search experience with us in this 6-weekend instructor-led course+live practice for senior engineers.

Built products for

Google
Qdrant
Samsung
Meta

Course overview

What's included:

🔥 Live Code Walkthroughs: Engage with code examples and implementations by coding along in real-time


🚀 Expert-led Sessions: Learn from industry veterans with over 2-decades of experience, providing practical knowhow and tricks


📚 Comprehensive Course Content: Gain a deep working knowledge of GenerativeAI technologies and their applications


🏃‍♀️ Intensive Week-long Case Study Sprint: Dive into practical projects like building your own LM, Customer Support Bot, and more


👨‍🏫 Guest Lectures: Exlusive lectures by the authors of Llama Index and Ragas

This course is for you if you are:

01

Senior Data Scientists, ML Engineers, CTOs, or Technical Leaders looking to improve an existing RAG system MVP

02

Aiming to scale your RAG system for better accuracy and faster response times

03

Ready to build nuanced, effective RAG systems across web, document and image retrieval

What you’ll get out of this course

Tailoring and Optimizing Search Solutions 🎯

  • Expertise in customizing search backends to address industry-specific query requirements
  • Ability to recognize and analyze query satisfaction disparities across user groups, enabling targeted improvements


Advanced Techniques and AI Management 🚀

  • Proficiency in implementing advanced techniques such as rerankers and ColBERT to enhance search quality while optimizing system performance
  • Strategies for effectively managing AI-related challenges, including hallucinations and ambiguous outputs (Y)

Building Expertise and Community 🌐 

  • Access to a network of professionals actively deploying production-ready search applications
  • Skills to construct and utilize an experimentation framework for identifying and validating enhancements

This course includes

21 interactive live sessions

Lifetime access to course materials

22 in-depth lessons

Direct access to instructor

1 projects to apply learning

Guided feedback & reflection

Private community of peers

Course certificate upon completion

Maven Satisfaction Guarantee

This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.

Course syllabus

Week 1

Sep 15

    Sep

    15

    Query Understanding and Profiling

    Sun 9/153:30 PM—5:00 PM (UTC)

    Finding & Measuring Hallucinations with Ragas

    1 item • Free preview

    Query Understanding and Profiling

    4 items

Week 2

Sep 16—Sep 22

    Sep

    18

    [Guest Lecture] Jerry Liu, CEO LlamaIndex on Multimodal RAG

    Wed 9/183:30 PM—5:00 PM (UTC)

    Sep

    19

    [Guest Lecture] Ofer, Vectara on RAG in a Box

    Thu 9/193:00 PM—4:00 PM (UTC)

    Sep

    21

    Automatic Prompting with DSPy

    Sat 9/213:30 PM—5:00 PM (UTC)

    Sep

    22

    Ingestion: Parsing, Chunking and Metadata Enrichment

    Sun 9/223:30 PM—5:00 PM (UTC)

    Testset Generation and LLM as a Judge with Ragas

    3 items • Free preview

    Practical Problems: Parsing, Chunking and Metadata Enrichment

    3 items

Week 3

Sep 23—Sep 29

    Sep

    24

    [Guest Lecture] Case Study - Building RAG Powered SEO Agency to $1M ARR

    Tue 9/243:30 PM—5:00 PM (UTC)

    Sep

    25

    [Guest Lecture] John Gilhully on Arize Phoenix with DSPy

    Wed 9/253:30 PM—4:45 PM (UTC)

    Embedding Models

    2 items • Free preview

    Sep

    28

    Neural IR: Embedding Models — Why, What, How

    Sat 9/283:30 PM—5:00 PM (UTC)

    Sep

    29

    Testset Generation and LLM as a Judge with Ragas

    Sun 9/293:30 PM—5:00 PM (UTC)

    Reducing LLM Latency, Improving Throughput & Reliability

    3 items

Week 4

Sep 30—Oct 6

    Oct

    5

    Scaling Dense Retrieval & Search Patterns

    Sat 10/53:30 PM—5:00 PM (UTC)

    Oct

    6

    Selecting a VectorDB: Considerations, Tradeoffs with Dhruv Anand

    Sun 10/63:30 PM—5:00 PM (UTC)

    Scaling Dense Retrieval

    3 items • Free preview

    Vector Databases / Search Engines

    2 items

Week 5

Oct 7—Oct 13

    Semi-structured Multi-Modal RAG

    2 items

    Oct

    9

    [Guest Lecture] DSPy in Finance, Alberto Romero, Director, Gen AI Platform Engineering at Citi

    Wed 10/93:30 PM—5:00 PM (UTC)

Week 6

Oct 14—Oct 20

    Oct

    16

    [Guest Lecture] Jo Bergum, What You See Is What You Search: Vision Language Models for PDF Retrieval

    Wed 10/168:30 AM—10:00 AM (UTC)

    Oct

    19

    Reverse Engineering Perplexity with Dhruv Anand

    Sat 10/193:30 PM—5:00 PM (UTC)

    Oct

    20

    Agentic RAG: Multi-Step Workflows

    Sun 10/203:30 PM—5:00 PM (UTC)

    Reverse Engineering Perplexity — Dhruv Anand

    0 items

    Structured Information Extraction — Dhruv Anand

    0 items

    Oct

    17

    Office Hours

    Thu 10/173:30 PM—4:30 PM (UTC)
    Optional

Week 7

Oct 21—Oct 24

    Oct

    22

    [Guest Lecture] Shreya Shankar on LLM as a Judge

    Tue 10/223:30 PM—5:00 PM (UTC)

    Oct

    23

    [Guest Lecture] Amir, Zenbase on LLM System Design and Scaling Feedback Loops

    Wed 10/233:30 PM—4:45 PM (UTC)

    Oct

    23

    [Guest Lecture] Atita Arora, Qdrant on IR for RAG — Vector DB Insider PoV

    Wed 10/2310:45 AM—12:15 PM (UTC)

    Oct

    24

    Course Recap & Sign Off!

    Thu 10/243:30 PM—5:00 PM (UTC)
    Optional

What people are saying

        Nirant's Search for RAG course is an absolute game-changer for anyone looking to master vector databases and Retrieval-Augmented Generation. As an NLP and AI expert, Nirant brings unparalleled expertise to the table, breaking down complex real-world scenarios for vector DB and RAG applications with insights you won't find elsewhere.
Dr. Pratik Desai

Dr. Pratik Desai

Co-founder, Kissan AI
        An invaluable resource for advancing skills in LLMs and search applications! This course offers extensive insights and practical techniques for building sophisticated chatbots, enhancing multimodal search functionality, implementing Tabular RAG (Retrieval-Augmented Generation), and leveraging LLMs for structured information extraction.
Ravi Theja

Ravi Theja

AI Engineer and Developer Advocate, LlamaIndex

Meet your instructor

Nirant Kasliwal

Nirant Kasliwal

Nirant Kasliwal is a notable AI Engineer with over 7 years of expertise in areas like chatbots, language models, and vector databases. He founded FastEmbed, an embedding library praised for its speed and utilized by companies including NVIDIA.


Recognized by AI luminaries such as Dr. Andrew Ng, Nirant is one of India's leading GenAI scientists. He has significantly contributed to AI education through projects like "Awesome NLP," a resource for engineers learning NLP, and continues to enhance AI accessibility and knowledge sharing.

Jithin James

Jithin James

Jithin James (jjmachan) is the founder of Exploding Gradients and creator of ragas, an open-source tool for evaluating RAG pipelines. He builds tools to help devs building with LLMs, especially those building with RAG pipelines. 


His work on ragas has significantly advanced the field of RAG evaluation, providing developers with crucial tools to enhance LLM-based applications. His contributions to open-source projects like BentoML have streamlined ML model deployment processes across the industry.


ragas is used by OpenAI for RAG evals. 

Dhruv Anand

Dhruv Anand

Dhruv Anand is the Founder and CEO of AI Northstar Tech, an innovation startup leveraging LLMs, vector embeddings, and databases to enhance Search and Recommendation systems, and build custom RAG applications.


An alumnus of Computer Science departments at Carnegie Mellon and IIT Kanpur, he has worked on ML models for Search products at both Google and Facebook.


As a consultant, Dhruv delivers optimized AI solutions to enterprises, specializing in Search algorithms and LLM fine-tuning. His expertise lies in architecting scalable AI systems that bridge academic research with industry demands.

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Course schedule

4-6 hours per week

  • Saturday & Sunday

    9:00pm - 10:30pm PST

    Hands-on sessions exploring each phase of the framework and guiding you through implementing enhancements in your RAG application.

  • Guest Lectures

    Coming soon

    We'll confirm the guest lecture schedule closer to the course date. We are onboarding e-commerce search and re-ranking veterans.

  • Capstone Project

    2 hours per week

    Project-specific, custom-built datasets spanning various industries -- ensuring you develop the targeted expertise required for your unique challenges. You can work on them async.

Free resource

How To Pitch An AI Project

When you pitch an internal project to your manager, you’re effectively selling the problem (and why it needs to be solved), the project (why it’s the ideal solution) and yourself as an AI engineer (why you are the right person to solve it).


Here's a step-by-step guide to effectively pitching a project to your manager.

Download free cheatsheet

Learning from a network of top AI professionals

Learning from a network of top AI professionals

Active hands-on learning

This course is designed to help you achieve enterprise-readiness in RAG with deep-dives and code walkthroughs.

Interactive and project-based

You’ll get hands-on experience creating next-generation AI solutions, from fine-tuned prompts to multi-modal systems, alongside other AI enthusiasts and data scientists.

Learn with a cohort of peers

Join a community of like-minded people who want to learn and grow alongside you

Frequently Asked Questions

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Search in the LLM Era for AI Engineers