"AI-Powered Search" Course Testimonials

Can't wait to start signal boosting before diving deeper into LTR.

A course packed with knowledge, Can recommend it to anybody who want to go beyond lexical search.

Tray and Doug shared many keen insights in AI Search. I great class for anyone looking to advance their understanding of Search principles in a meaningful way.

Great course for anyone who wants to enter the world of AI applied to search, learning different types of algorithms and their applicability in real-world contexts.

A course that covered search topics extensively, from traditional to modern methods. The amount of work that went into creating it and the ground it covers in about a month is phenomenal.

Really solid course—materials were clear and easy to go through. The pre-filled labs made it simple to get hands-on practice without wasting time on setup. Definitely a useful experience.

’m relatively new to the topic of search, and this course gave me both a solid overview of the common technologies and a deep dive into important concepts and tools. I would definitely recommend it.

Broad overview of current techniques from leading experts. The course is especially valuable if you already have a good foundation in the area and bring your own questions to the speakers and cohort.

The latest and greatest from the fast-changing field of search and information retrieval. The guests are bringing the latest developments and technologies from their industrial experience, not academic papers. I enjoyed the course very much

One of the best course on search and retrieval. Covers the full spectrum from BM25 to semantic query understanding all with hands on labs. If you're building search, or RAG systems don't skip this one. rushit MARCH-2026 Enterprise Architect · TTU

This is a great course even for someone who has experience with search for years but wants to learn the latest search technologies. It's a good mix of materials covering the theory that is in a level sufficient to understand how to use them in practice.

The course is very relevant to current industry problems in Search. I have dealt with the issues discussed in this course but lacked a direction. With this course I feel informed and confident of making suggestions to improve Search experience at my current company

I came to the AI search domain completely new, but walked away with a good grounding and appreciation of what it takes to build good search. The content and sessions are very well structured, plenty of labs to get your teeth into and help is always on hand from Trey and Doug.

I took this course as a software engineer working on search retrieval systems. Bag of documents techniques, wormhole vectors, and search personalisation were all directly relevant to my day-to-day work. The course is practical rather than academic. Would recommend to any engineer working on search.

Solid overview of search approaches, from traditional methods to newer innovations. The concepts were well-explained and easy to follow. The external speakers were particularly valuable, bringing different perspectives and practical insights to the discussion. Filipa MARCH-2026 Data Scientist · OLX

The course covers a lot of ground in 5 weeks, and I learned a lot. The weekly office hours provided a wonderful opportunity to pick the brains of Trey and Doug on specific topics relevant to the problems I was tackling at my workplace. And to top it all, the instructors are super-nice people. Highly recommend the course.

AI-Powered Search by @treygrainger & @softwaredoug & AI-Powered Search: the most in-depth course I've taken for leveling up retrieval skills.

Moved me from "throw embeddings at it and hope" to actually understanding query intent, ranking signals, and production RAG patterns.

Hands-on labs + expert guest speakers from Qdrant, OpenSearch, Superlinked made it incredibly practical.

Essential for anyone serious about search & RAG. Next cohort in March 👉 https://lnkd.in/gxnkckZa

I found this course to be exactly what was missing from other AI engineering courses I’ve taken. Extremely thorough topic coverage (Agentic search, rerankers, hybrid search, Query understanding to name a few), and a great mix of teaching conceptual understanding and providing hands on practice. This course allows you to understand not just the what, but the why behind the process. If you're building AI systems and feel like you're missing some foundational pieces, this course will fill those gaps.

Trey and Doug have put together and delivered an excellent course that includes both breadth and depth on the topic of AI Powered Search. I appreciated how they brought some dense topics to life through examples and interactive labs. There were plenty of opportunities to ask questions throughout the lectures and they offered weekly office hours to go in depth on topics of interest to the group. I recommend this course to any search practitioners that are looking to evolve their search products with leading edge techniques. I especially recommend this course to Search Product Managers who are new to the AI-Powered Search landscape that need a grounding in the technology.

After six months of intensive AI upskilling and building RAG systems, I thought I had a decent handle on retrieval . Then I took the course AI-Powered Search from Maven , and realized how much I'd been missing. Trey Grainger and Doug Turnbull go far beyond the "download embeddings from Hugging Face and hope for the best" approach. They systematically dismantle the naive assumption that hybrid search (BM25 + cosine similarity) is "good enough." As Trey put it in the semantic query understanding session: most people just throw embeddings and keywords at the search engine and hope it comes up with the right thing. This course is about challenging that paradigm.

The definitive way to stay current with modern retrieval. Period. I've taken both iterations of this course, and it remains the single best investment I've made in my professional development as a search engineer. The landscape of AI-powered search and RAG is shifting so fast that staying ahead of the curve usually means piecing together scattered blog posts and pre-print papers. Trey and Doug have done the hard work of curation, weaving together the essential mental models and the latest techniques into a coherent, actionable framework. The only place where saying "We need to talk about your late interaction" is a compliment. Verdict: 10/10 would compute cosine similarity with this group again.

Just wrapped up the Maven Course: AI-Powered Search - Modern Retrieval for Humans & Agents, and wow—it's a game-changer that left me buzzing with excitement and clarity!​I dove in initially for the product/search angle, but this course delivered extreme value for technical deep-dives and business insights, empowering owners to max out search in development. The content is razor-sharp up-to-date, unpacking complex solutions like personalized behavioral embeddings, agentic search paradigms, and ML ranking with crystal-clear intuition that ties every piece together—no more black-box confusion.​What blew me away? The perfect learning intensity, permanent access to recordings/notebooks/slides, and an amazing community of top-tier lecturers/peers —plus live 'office hours' for diving into any question, even off-scope. It's not just theory; it's hands-on mastery bridging demos to production systems that actually crush relevance for humans and agents.​If you're a dev, founder, or search-curious pro, this is your edge in the AI retrieval revolution—join!

I took this course because, even after doing search work for a couple of years (mostly self-taught), I kept running into the same problem: I could research concepts online, but the pieces didn’t always click into a production-grade mental model. This class was exactly what I needed. Doug and Trey bridge the gap between theory and real systems in a way that’s hard to get from blog posts and docs, especially the practical “how” behind query understanding, making hybrid retrieval behave predictably, and turning behavioral signals into an iteration loop that actually improves ranking. The biggest value for me was the experience transfer: the nuance, the “why,” and the decision-making that comes from having built and tuned search at scale. The live format and Q&A made a huge difference, you can bring your real problems and get grounded guidance. The hands-on labs were also excellent: a reusable set of notebooks and techniques I can apply to my own stack. If you’re building search/RAG systems and feel like you’re missing the foundations (or the production intuition), this course fills that gap fast.

Excited to share that I’ve completed Maven’s AI-Powered Search course .
Over 5 weeks, I went deep on what it takes to build modern, relevance-driven search systems—from classic IR foundations to LLM-era retrieval and ranking.
A few key takeaways I’m walking away with:
Relevance is a product problem as much as a modeling problem: user behavior insights, implicit signals, and evaluation loops matter as much as architecture.
Hybrid retrieval is the default: mixing sparse + dense (e.g., MiniCOIL-style approaches) and tuning the blend for real traffic.
Ranking is the real lever: LTR, click models, bias-aware learning, and active learning to keep improving without endless labeling.
RAG done right: interleaving strategies, retrieval quality, and grounding—because “more context” ≠ “better answers.”
Performance + UX: vector search optimization, semantic query understanding, semistructured embeddings, and agentic search patterns.
Big thanks to the speakers who shared practical lessons and real-world war stories Doug Turnbull and Trey Grainger🙏
Next up: applying these ideas to AI-powered conversational search—improving relevance, grounding, and measurable user outcomes.

I recently completed the AI-Powered Search: Modern Retrieval for Humans & Agents course by Trey Grainger and Doug Turnbull on Maven.

What stood out most was how the course builds a structured framework for thinking about retrieval and relevance, two aspects that are often underestimated but critical for successful AI systems.

This course helped me connect many dots:
• how RAG systems really work beyond simple embeddings
• how to systematically improve search relevance instead of relying on trial and error
• how user intent, context, and domain knowledge influence retrieval quality
• when to use lexical, semantic, hybrid, and re-ranking approaches
• why retrieval is often the biggest lever for improving AI applications

I would highly recommend this course to:
• AI Engineers working with RAG systems
• Solution Architects designing AI-powered products
• Anyone who wants to go deeper than prompt engineering and understand how modern AI search really works

If you are serious about building high-quality AI applications, understanding retrieval is a game changer.
👉 The next cohort starts on July 1st; I highly encourage you to join. Link in the comments.

Can't wait to start signal boosting before diving deeper into LTR.

A course packed with knowledge, Can recommend it to anybody who want to go beyond lexical search.

Tray and Doug shared many keen insights in AI Search. I great class for anyone looking to advance their understanding of Search principles in a meaningful way.

Great course for anyone who wants to enter the world of AI applied to search, learning different types of algorithms and their applicability in real-world contexts.

A course that covered search topics extensively, from traditional to modern methods. The amount of work that went into creating it and the ground it covers in about a month is phenomenal.

Really solid course—materials were clear and easy to go through. The pre-filled labs made it simple to get hands-on practice without wasting time on setup. Definitely a useful experience.

’m relatively new to the topic of search, and this course gave me both a solid overview of the common technologies and a deep dive into important concepts and tools. I would definitely recommend it.

Broad overview of current techniques from leading experts. The course is especially valuable if you already have a good foundation in the area and bring your own questions to the speakers and cohort.

The latest and greatest from the fast-changing field of search and information retrieval. The guests are bringing the latest developments and technologies from their industrial experience, not academic papers. I enjoyed the course very much

One of the best course on search and retrieval. Covers the full spectrum from BM25 to semantic query understanding all with hands on labs. If you're building search, or RAG systems don't skip this one. rushit MARCH-2026 Enterprise Architect · TTU

This is a great course even for someone who has experience with search for years but wants to learn the latest search technologies. It's a good mix of materials covering the theory that is in a level sufficient to understand how to use them in practice.

The course is very relevant to current industry problems in Search. I have dealt with the issues discussed in this course but lacked a direction. With this course I feel informed and confident of making suggestions to improve Search experience at my current company

I came to the AI search domain completely new, but walked away with a good grounding and appreciation of what it takes to build good search. The content and sessions are very well structured, plenty of labs to get your teeth into and help is always on hand from Trey and Doug.

I took this course as a software engineer working on search retrieval systems. Bag of documents techniques, wormhole vectors, and search personalisation were all directly relevant to my day-to-day work. The course is practical rather than academic. Would recommend to any engineer working on search.

Solid overview of search approaches, from traditional methods to newer innovations. The concepts were well-explained and easy to follow. The external speakers were particularly valuable, bringing different perspectives and practical insights to the discussion. Filipa MARCH-2026 Data Scientist · OLX

The course covers a lot of ground in 5 weeks, and I learned a lot. The weekly office hours provided a wonderful opportunity to pick the brains of Trey and Doug on specific topics relevant to the problems I was tackling at my workplace. And to top it all, the instructors are super-nice people. Highly recommend the course.

AI-Powered Search by @treygrainger & @softwaredoug & AI-Powered Search: the most in-depth course I've taken for leveling up retrieval skills.

Moved me from "throw embeddings at it and hope" to actually understanding query intent, ranking signals, and production RAG patterns.

Hands-on labs + expert guest speakers from Qdrant, OpenSearch, Superlinked made it incredibly practical.

Essential for anyone serious about search & RAG. Next cohort in March 👉 https://lnkd.in/gxnkckZa

I found this course to be exactly what was missing from other AI engineering courses I’ve taken. Extremely thorough topic coverage (Agentic search, rerankers, hybrid search, Query understanding to name a few), and a great mix of teaching conceptual understanding and providing hands on practice. This course allows you to understand not just the what, but the why behind the process. If you're building AI systems and feel like you're missing some foundational pieces, this course will fill those gaps.

Trey and Doug have put together and delivered an excellent course that includes both breadth and depth on the topic of AI Powered Search. I appreciated how they brought some dense topics to life through examples and interactive labs. There were plenty of opportunities to ask questions throughout the lectures and they offered weekly office hours to go in depth on topics of interest to the group. I recommend this course to any search practitioners that are looking to evolve their search products with leading edge techniques. I especially recommend this course to Search Product Managers who are new to the AI-Powered Search landscape that need a grounding in the technology.

After six months of intensive AI upskilling and building RAG systems, I thought I had a decent handle on retrieval . Then I took the course AI-Powered Search from Maven , and realized how much I'd been missing. Trey Grainger and Doug Turnbull go far beyond the "download embeddings from Hugging Face and hope for the best" approach. They systematically dismantle the naive assumption that hybrid search (BM25 + cosine similarity) is "good enough." As Trey put it in the semantic query understanding session: most people just throw embeddings and keywords at the search engine and hope it comes up with the right thing. This course is about challenging that paradigm.

The definitive way to stay current with modern retrieval. Period. I've taken both iterations of this course, and it remains the single best investment I've made in my professional development as a search engineer. The landscape of AI-powered search and RAG is shifting so fast that staying ahead of the curve usually means piecing together scattered blog posts and pre-print papers. Trey and Doug have done the hard work of curation, weaving together the essential mental models and the latest techniques into a coherent, actionable framework. The only place where saying "We need to talk about your late interaction" is a compliment. Verdict: 10/10 would compute cosine similarity with this group again.

Just wrapped up the Maven Course: AI-Powered Search - Modern Retrieval for Humans & Agents, and wow—it's a game-changer that left me buzzing with excitement and clarity!​I dove in initially for the product/search angle, but this course delivered extreme value for technical deep-dives and business insights, empowering owners to max out search in development. The content is razor-sharp up-to-date, unpacking complex solutions like personalized behavioral embeddings, agentic search paradigms, and ML ranking with crystal-clear intuition that ties every piece together—no more black-box confusion.​What blew me away? The perfect learning intensity, permanent access to recordings/notebooks/slides, and an amazing community of top-tier lecturers/peers —plus live 'office hours' for diving into any question, even off-scope. It's not just theory; it's hands-on mastery bridging demos to production systems that actually crush relevance for humans and agents.​If you're a dev, founder, or search-curious pro, this is your edge in the AI retrieval revolution—join!

I took this course because, even after doing search work for a couple of years (mostly self-taught), I kept running into the same problem: I could research concepts online, but the pieces didn’t always click into a production-grade mental model. This class was exactly what I needed. Doug and Trey bridge the gap between theory and real systems in a way that’s hard to get from blog posts and docs, especially the practical “how” behind query understanding, making hybrid retrieval behave predictably, and turning behavioral signals into an iteration loop that actually improves ranking. The biggest value for me was the experience transfer: the nuance, the “why,” and the decision-making that comes from having built and tuned search at scale. The live format and Q&A made a huge difference, you can bring your real problems and get grounded guidance. The hands-on labs were also excellent: a reusable set of notebooks and techniques I can apply to my own stack. If you’re building search/RAG systems and feel like you’re missing the foundations (or the production intuition), this course fills that gap fast.

Excited to share that I’ve completed Maven’s AI-Powered Search course .
Over 5 weeks, I went deep on what it takes to build modern, relevance-driven search systems—from classic IR foundations to LLM-era retrieval and ranking.
A few key takeaways I’m walking away with:
Relevance is a product problem as much as a modeling problem: user behavior insights, implicit signals, and evaluation loops matter as much as architecture.
Hybrid retrieval is the default: mixing sparse + dense (e.g., MiniCOIL-style approaches) and tuning the blend for real traffic.
Ranking is the real lever: LTR, click models, bias-aware learning, and active learning to keep improving without endless labeling.
RAG done right: interleaving strategies, retrieval quality, and grounding—because “more context” ≠ “better answers.”
Performance + UX: vector search optimization, semantic query understanding, semistructured embeddings, and agentic search patterns.
Big thanks to the speakers who shared practical lessons and real-world war stories Doug Turnbull and Trey Grainger🙏
Next up: applying these ideas to AI-powered conversational search—improving relevance, grounding, and measurable user outcomes.

I recently completed the AI-Powered Search: Modern Retrieval for Humans & Agents course by Trey Grainger and Doug Turnbull on Maven.

What stood out most was how the course builds a structured framework for thinking about retrieval and relevance, two aspects that are often underestimated but critical for successful AI systems.

This course helped me connect many dots:
• how RAG systems really work beyond simple embeddings
• how to systematically improve search relevance instead of relying on trial and error
• how user intent, context, and domain knowledge influence retrieval quality
• when to use lexical, semantic, hybrid, and re-ranking approaches
• why retrieval is often the biggest lever for improving AI applications

I would highly recommend this course to:
• AI Engineers working with RAG systems
• Solution Architects designing AI-powered products
• Anyone who wants to go deeper than prompt engineering and understand how modern AI search really works

If you are serious about building high-quality AI applications, understanding retrieval is a game changer.
👉 The next cohort starts on July 1st; I highly encourage you to join. Link in the comments.