Shubham Saboo

Semantic Search with Low-Code

Tenstorrent

Jina AI

Arcesium

AI Author, Product Manager at Tenstorrent, Founder of Unwind AI

Create a powerful RAG pipeline for semantic search with a few lines of Python code

Build RAG pipeline for semantic search

Ingest data from multiple sources into a vector database with minimal Python code

Integrate data from multiple sources

Setup and deploy a semantic search pipeline end-to-end using Embedchain and Streamlit

Deploy your application in a few clicks

Become the RAG Product Leader Your Company Needs Companies are racing to integrate AI features that truly deliver value. To stay ahead, you must master the art of building Retrieval-Augmented Generation (RAG) products that continuously improve—no luck or guesswork required. 6 Weeks to Transform Your AI Product Sense This course removes the mystery and turns RAG from a risky experiment into a structured, data-driven practice. You’ll learn how to pinpoint what’s working, diagnose what’s not, and steadily raise the bar on performance and user satisfaction. What You’ll Learn, Week-by-Week Week 1: Fundamentals &amp; Synthetic DataEstablish a repeatable system for evaluating and improving RAGGenerate synthetic datasets for fast, meaningful experimentsMaster precision, recall, and essential retrieval metricsLay the groundwork for a self-reinforcing improvement cycle Week 2: Segmentation &amp; AnalysisBreak down complex query patterns and user behaviorsSpot inventory gaps and capability shortfalls quicklyUse clustering, classification, and dashboards to track progressSet improvement priorities that deliver maximum impact Week 3: Structured Data &amp; MultimodalityExtract and leverage metadata for more accurate retrievalHandle images, documents, and tables without losing qualityCreate specialized indexes and blend them effectivelyApply earlier techniques to broaden RAG capabilities Week 4: Query Routing &amp; Tool SelectionIntegrate multiple search methods into a single coherent productRoute queries intelligently using parallel function callsTurn tool selection into a quantifiable, improvable taskSecure higher success probabilities end-to-end Week 5: Representations &amp; Fine-TuningMove beyond generic embeddings into fine-tuned, domain-specific vectorsDevelop re-rankers that drastically improve retrieval qualityDiscover how small amounts of targeted data can yield big gainsShorten the experimentation cycle for continuous improvement Week 6: Product UX &amp; Feedback LoopsDesign user interfaces that collect feedback effortlesslyUse streaming and visual hints to make latency feel negligibleImplement citations, follow-up actions, and chain-of-thought promptingAdd validators and checks to ensure reliability at scale Why This MattersRAG has become a core differentiator for AI-driven products. The strategies you learn here will never go out of style. As tools and models evolve, you’ll know how to integrate them, measure their impact, and refine your approach—over and over. What’s IncludedProven evaluation frameworks that demystify “is it working?”Techniques to handle more data types, faster and more accuratelyMethods for continuous feedback collection and refinementInsights that adapt as the AI landscape changes About Your Instructor Jason Liu is a seasoned AI consultant who has guided ai engineering at Stitch Fix, and high-growth startups from Seed stage to public companies. He also advises many of today's generative AI startups. Jason specializes in transforming vague AI ambitions into concrete, measurable practices that deliver real business value. Exclusive Bonuses ($1,500+ Value)$500 Cohere credits$200 LanceDB credits + free Lance Cloud access$500 Modal Labs credits6 months of Notion AI Plus3 months of Braintrust access ($250 value) Who Should ApplyProduct teams integrating AI features into real software productsAI engineers moving from prototype to production-grade performanceAnyone committed to developing true “AI product sense”—not just hype You Only Pay After Acceptance &amp; Full Refund GuaranteeWe’re so confident in the course’s value that if you don’t see meaningful improvements in your product’s performance within five weeks of starting, we’ll refund you fully. Don’t let uncertainty stall your AI progress. Join a small cohort of dedicated practitioners, learn the proven frameworks, and build the RAG systems your users will love.

Acquire the skills & confidently improving and iterate on RAG applications

Provide a brief description for each module as a way to sell the value of what students will be learning throughout the course.

First Module

Second Module

Third Module

Course syllabus

Workshops covering each step of the playbook and helping you build process improvements in your RAG application

Tuesday: Workshops

The first half hour will be interactive breakout sessions, and the closing half-hour each week is Q&amp;A

Wednesday: Office Hours + Breakout Sessions

Guest instructors covering key topics in both innovative theory and practical applications in RAG system development.

Thursday: Guest Speakers

Course Schedule Each Week

A product leader, engineer, or data scientist looking to move beyond ad-hoc RAG prototypes into scalable, production-grade AI solutions.

A professional who understands LLM basics but wants a repeatable, data-driven methodology to improve retrieval relevance, latency, and user

Eager to create feedback loops that continuously refine and enhance the quality of RAG applications as models, data, and user needs evolve.

This Course Is For You If You Are

By the end of this course, participants will be able to

Implement the Data and Evals Flywheel approach to continuously develop and improve RAG applications—breaking free from guesswork and relying on measurable, iterative enhancements.

Adopt a Systematic, Data-First Methodology

Quickly assess your retrieval systems using precision and recall metrics, identify bottlenecks, and confidently validate changes without sinking into endless trial-and-error cycles.

Run Fast, Unit-Test-Like Evaluations

Generate and utilize synthetic data sets to speed up experimentation, enabling you to test new approaches, embeddings, and architectures before committing full resources.

Leverage Synthetic Data for Rapid Iteration

Apply fine-tuning strategies for embedding models to boost search relevance and explore hard negative examples to further sharpen retrieval performance.

Master Fine-Tuning & Hard Negative Mining

Use query classification and segmentation techniques to pinpoint exactly where your RAG system falls short—whether it’s due to limited inventory or insufficient capabilities.

Classify Queries & Identify Bottlenecks

Create tailored indices for documents, images, tables, SQL databases, and more. Learn when and how to fuse or layer these indices to handle complex retrieval tasks elegantly.

Design Specialized Indices for Multiple Modalities

Implement synthetic text chunk generation and strategic summarization methods to improve retrieval results, ensuring end-users get clear, concise, and contextually rich answers.

Enhance Retrieval with Summarization & Chunking

Develop systems that intelligently route queries to the right indices, tools, or pipelines. Blend and fuse indices effectively to handle nuanced, multi-step queries.

Implement Query Routing & Index Fusion

Evaluate the performance of your routing logic and each individual index separately. Gain the nuance to fine-tune global system performance and local retrieval accuracy in tandem.

Optimize Both Global & Local Performance

Design explicit and implicit feedback mechanisms—capturing user signals, automating re-labeling, and applying improvements in real-time to keep your RAG systems on an upward trajectory.

Integrate Feedback Loops for Continuous Improvement

Follow a repeatable process to continually evaluate and improve your RAG application

Jason Liu

Cohort 1

Cohort 2

Welcome to the most technically rigorous and hands-on Large Language Model (LLM) application course available today. This isn't just another AI course – it's your gateway to mastering the art and science of deploying production-grade LLM solutions that stand out in the real world. As part of Maven's Top-Rated Content, this course is designed for those who have already mastered the basics of RAG, cosine similarity, vector databases, and LLMs. We'll take you to the next level, focusing on practical aspects of packaging and deploying these models in real-world production environments. For cohort members joining this intensive learning experience, here's what you get: - 6 weeks of in-depth content- Weekly office hours for personalized guidance- Real-world projects and challenging assignments- Guest Lectures by Leading AI Professionals- Continued support post-graduation- Lifetime access to course materials What You'll Master: Course Highlights Agents: Forget CrewAI or Autogen, build your won Agents from scratch. Learn what it takes to make an Agent from the ground. Contribute to Open Source community. Advanced RAG Solutions: Dive into enterprise-level RAG architectures and learn how to build and implement semantic caching from scratch using GCP and Redis. LLM Hosting and Deployment: Gain insights into best practices for hosting Large Language Models (LLMs) in diverse production settings, creating inference endpoints, and deploying LLMs on serverless platforms. Continual Pre-Training and Fine-Tuning: Explore advanced techniques for continual pre-training, fine-tuning LLMs, and mitigating catastrophic forgetting. Learn how to build a data pipeline for pre-training, apply causal language modeling, and leverage scaling laws. Model Merging and Mixture of Experts: Master techniques for merging multiple models to enhance their collective capabilities, including the Mixture of Experts (MoE) approach. Learn to use tools like mergekit for efficient model merging. Quantization Methods: Discover techniques to reduce model size while maintaining performance, crucial for deployment in resource-constrained environments. Inference Speed Optimization: Learn strategies to accelerate inference speeds for real-time language processing, ensuring efficient and responsive AI systems. Responsible AI Implementation: Explore ethical AI development using guardrails like NeMo, Colang, and Llama Guard to ensure AI systems align with responsible AI principles. Agentic RAG and Chunking Strategies: Implement advanced semantic chunking techniques and explore AI agent frameworks like AutoGen to enhance the capabilities of RAG systems. DSPy and Knowledge Graphs: Learn to create and utilize knowledge graphs effectively, mastering DSPy as an alternative prompting approach for structured data handling and enhanced AI interaction. Throughout the course, we will analyze state-of-the-art AI products, reverse-engineering some through Python. As a bonus, you'll have access to experimental products being developed at Traversaal.ai, my startup, allowing you to stay at the forefront of advancements in the field. Prerequisite: You should have coding experience of building RAG Solution. Understanding of Encoders and Decoders and some knowledge of Cloud Solutions and APIs. If you feel the need for a more foundational course, consider checking out my other offering on LLMs: Building LLM Applications (https://maven.com/boring-bot/ml-system-design).

Go Beyond Basic Frameworks: Build and Deploy Production-Grade AI Solutions

Sundays

Work in teams to build solutions, this requires engagement with other team members

Weekly projects

Course schedule

Machine Learning Engineer exploring different techniques to scale LLM solutions

Researcher, who would like to delve in to various aspects of open-source LLMs

Software Engineer, looking to learn how to integrate AI into their products

This course is for you if you are a:

What you’ll get out of this course

Understand and implement complex AI architectures, including enterprise-level RAG systems and agentic RAG strategies. You will also dive deep into the Mixture of Experts (MoE) technique and other model merging strategies to enhance the capabilities of your AI systems.

Advanced AI Architectures

From building semantic caches using GCP and Redis to deploying LLMs on serverless platforms like AWS Bedrock, you'll learn the practical skills to deploy and manage AI applications in real-world scenarios.&nbsp;

Practical Skills for Deployment

Acquire advanced techniques for fine-tuning LLMs, enabling you to adapt these models to specific tasks or domains and enhance their performance in targeted applications.

Fine-Tuning Expertise

Explore strategies for exploring and optimizing inference speeds, ensuring that your language models perform efficiently in real-time scenarios, a crucial skill for deploying responsive and scalable applications.

Efficient Inference Processing

Understand the importance of ethical AI development and learn to implement guardrails using tools like NeMo, Colang, and Llama Guard to ensure your AI systems align with responsible AI principles.

Knowledge of Responsible AI

Build and Optimize Production-Grade RAG and LLM Applications: Master Advanced Techniques for Scalable, Secure, and Low-Latency AI Solutions

Cohort 4

Hamza Farooq

Cohort 3

Cohort 5

If you’re a software engineer or data scientist, chances are you’ve built or seen a proof of concept (POC) for a generative AI app. It’s exciting at first—an impressive demo that showcases the potential of large language models. But here’s the problem: that’s where it usually stops. These POCs often fail to scale into reliable, production-ready applications. The result? Endless iteration cycles, unreliable outputs, and frustration as teams struggle in what we call “POC purgatory.” It doesn’t have to be this way. With the right tools, principles, and processes, you can move beyond POCs and build LLM-powered applications that work—reliably, robustly, and at scale. In this course, we’ll show you how. You’ll not only learn the technical skills to design, test, and deploy generative AI applications but also develop the product mindset needed to iterate and improve rapidly. This isn’t just about building something cool—it’s about creating systems that deliver real value, solve real problems, and scale with confidence. What to ExpectThis is a highly interactive course designed to get you results. Across live workshops, discussions, and hands-on projects, you’ll build your skills while solving real-world challenges. Expect: •	Live coding sessions to design and refine LLM applications.•	Iteration exercises to explore how small changes can dramatically improve reliability.•	Discussions on user-centric development and integrating feedback loops.•	Expert-led Q&amp;As to help you tackle your toughest challenges. What You’ll Learn •	How to transform POC demos into production-grade applications.•	Strategies for managing non-determinism and optimizing outputs through prompt engineering.•	How to monitor, log, and debug AI systems to ensure reliability and performance.•	Best practices for handling structured outputs and integrating function calling into your applications.•	Building workflows for iterative development and experimentation.•	Practical skills through hands-on app development, including creating a Generative AI app to query PDFs. Bonus Perks All students will receive $1,000 worth of Modal credits to help power their AI applications, with more API credit announcements coming soon. Who This Course Is For This course is designed for: •	Data scientists and machine learning engineers who are sick of unreliable POCs and want to ship reliable LLM applications.•	Software engineers who want to learn how to build generative AI systems and master the LLM software development lifecycle. Who This Course Is Not For This course is not for those who are: •	Looking for an introductory course in AI—some programming knowledge is required.•	Expecting ready-made solutions for AI problems without active participation.•	Unable to commit to hands-on projects, live discussions, and iterative development exercises. Why Take This Course By the end of this course, you’ll: •	Understand the first principles of generative AI development.•	Gain hands-on experience building robust, scalable applications.•	Learn how to troubleshoot, monitor, and optimize AI systems for production.•	Develop workflows to iterate and refine AI applications effectively.•	Leave with the confidence to deploy reliable AI systems at scale.

Ground principles that underpin successful GenAI app development

Live in person sessions. We'll finalize session times by mid-November.

Mondays and Wednesdays

To ensure hands-on practical time, there will be project work to complete throughout the course.

Data scientists, machine learning engineers who are sick &amp; tired of seeing and building prototypes &amp; want to ship reliable LLM applications

Software engineers who want to learn how to build Generative AI systems and learn the LLM software development lifecycle.

Who is this course for

You’ve seen impressive generative AI demos—but how do you take them to production? In this course, you’ll learn the exact workflows, tools, and engineering practices needed to turn concepts into reliable, scalable applications that deliver real value.

Move beyond POCs and build production-ready AI systems

Struggling with inconsistent outputs? Through interactive exercises, you’ll explore proven techniques for optimizing prompts and ensuring your AI delivers actionable results in production settings.

Master prompt engineering for reliable results

When your AI application breaks, you’ll know exactly how to fix it. We’ll walk you through real-world debugging sessions, monitoring techniques, and tools to ensure your systems stay reliable at scale.

 Learn to debug and monitor AI systems like a pro

Want your AI to produce precise, usable results? You’ll learn how to handle structured outputs and function calls, making your applications smarter and more user-friendly.

Build smarter applications with structured outputs

Stop wasting time in endless iteration cycles. Through guided projects, you’ll discover how to set up workflows that refine your applications faster and with less frustration.

Accelerate your development process with iterative workflows

Tired of theoretical knowledge? You’ll build a fully functional app that uses text and image models to query PDFs, giving you practical experience solving complex real-world problems.

Develop hands-on expertise by building GenAI and LLM apps

Whether you’re looking to lead AI projects, impress stakeholders, or level up your technical expertise, this course gives you the tools to stand out as an AI/ML practitioner.

Advance your career with practical, proven techniques

These aren’t just skills for demos—they’re skills for delivering scalable, production-ready systems that solve real-world business challenges.

Solve Real Problems at Scale

Leave the course with a project you can add to your portfolio, demonstrate to your boss, or use to showcase your ability to deploy reliable AI systems.

Showcase Tangible Results

Build LLM-powered software reliably & from first principles. Learn the GenAI software development lifecycle: agents, evals, iteration & more

Hugo Bowne-Anderson

Host of Vanishing Gradients podcast; ex-Outerbounds; expert in DS/ML.

Stefan Krawczyk

Ex-Stitch Fix; 13+ Years building / productionizing data, ML, and AI

The challenge: most people who don't have a technical background do not understand the reasons why products like ChatGPT, Claude, or Cursor respond the way they do. Because of this, they also don't understand how to get what they want and need out of AI tools. A quick story: my sister is a physician-scientist. She has an MD-PhD. She does cutting edge scientific research and works with data on the daily. Last year, we were on a video call where she mentioned being unimpressed with ChatGPT's response to her work question. She told me what she was trying to do, and I went, "wait, AI could definitely handle this - what did you type?" I flipped the screen, pointed it at my monitor, and proceeded to have a conversation with ChatGPT that got her all the answers she needed in just a few minutes. This "capability unlock" has gone on to save her at least an hour in her work every day. DAILY. And she was mere seconds away from having given up on it, thinking AI wasn't yet good enough to get the job done. And this is when I realized, every non-technical user needs to understand not just "prompt engineering," as you've heard described elsewhere.It's more fundamental than that. It's...• The reasons and treasons of why AI does or doesn't give you a good answer• The nuances of technical limitations (safety, length, 'correct' output)• The incredible power of chain of thought &amp; reflective techniques This 3-day intensive will cover the above, and so much more, with the outcome of getting you to become fluent in talking to AI systems, including large language models (like ChatGPT/GPT-4o out of OpenAI, Claude out of Anthropic, LLaMA out of Meta) and image generation models (like Midjourney, DALL-E, and Stable Diffusion). And when I say "fluent," I mean something very specific. Just as language fluency isn't about memorizing phrases from a travel guidebook, AI fluency isn't about collecting prompts from Twitter. True fluency means understanding the deeper patterns of interaction -- knowing when to push, when to pivot, and most importantly, why. Consider this: most courses teach you what to say to AI. This course teaches you how to think about AI conversation. It's the difference between learning to order coffee in French versus understanding why French speakers structure their thoughts the way they do. What you'll get out of the 3 day intensive - Day 1Mental Models &amp; Mechanics• What AI knows versus what it can do; how AI reads, thinks, and speaks, + biases and limits• The hidden landscape of latent space, embeddings, and vectors - what does it all mean for your usage?• Differences in AI paradigms, LLM models, genAI models, and the applications that sit on top of them Day 2Conversations &amp; Debugging• Ask the unaskable: form questions that inspire LLMs to answer you; learn the DRIVE context framework• Use layered prompting and multi-turn conversations for your most advanced queries and topics• Get AI to "think out loud," giving you more data, more insights, and the ability to iterate over time Day 3Advanced Applications• Use reflective and agentic techniques + products to get AI "working for you" - soon, you'll have a team• Get multi-modal - combine models, model types, and apps to generate fast, impactful outputs• &amp; more: analysis, tools, fine-tuning, hallucinations, ethical considerations, and real world use And more• retrieval augmented generation (RAG), code use, integration + extensions, voice-based nuances• chain of thought, reflection, context windows, and other AI concepts explained• how do apps make use of LLMs, how is AI being integrated by companies across tech At the end of this 3-day intensive, you'll...• be able to transform vague ideas into incredible outputs, whether you're brainstorming, working with data and statistics, coding with AI, using it for workflow, asking it knowledge questions, utilizing it for research, adopting it as a content companion, or using it in an academic setting• troubleshoot and debug AI responses - you'll know when you've hit a real limit versus a bug or user error, unlocking the boundaries of what AI can do before anyone else -- no matter the field you're in• future-proof your skills - because you're going to have a deep understanding of how these products work, you'll remain capable of using them as the models are continuously updated and evolved, even as the rapid pace of AI development continues It's about harnessing the power of this transformative technology to amplify your own creativity, productivity, and impact on the world. Get ready to go beyond "prompt engineering" and embark on a journey to truly master the art and science of communicating with AI. Are you ready to unlock the conversation?

You're probably bad at talking to AI. I'll make it your smartest friend.

3 days of live classes

Can't make it to the live classes or want to watch them again later? We'll make each day's content available same-day for all students.

Access recordings forever

There will be several projects and optional group workshops to turn the content into usable muscle - attend live for instructor feedback, or access projects, examples, and templates at your own pace - including permanent access after the course ends.

Daily projects (optional)

Non-technical or semi-technical professionals (throughout knowledge work) looking to gain an upper hand in this AI moment.

Anyone who has felt the gap between AI's potential, what they hear about what it can do, and their ability to access the upside.

NOT for engineers who already use LLM copilots every day. NOT for people looking for quick prompt templates.

You'll learn about the cognitive architecture that underpins not just LLMs, but the way that products and apps are built around them. These patterns will work across AI models and will continue working as systems evolve.

Instead of AI prompt templates, learn conversational design patterns

It's hard for even the most AI-passionate people to integrate and reliably, consistency use AI products. I'll give you systems, shortcuts, and methods for making it second nature -- we'll build your AI muscle.

Instead of treating ChatGPT like a magic answer box, you'll make it a constant companion

"Why does it hallucinate here, but not there? When should I restart a conversation versus redirect it? Why does it sometimes forget what we just discussed? How does its sourcing work? When is more context good versus bad?" You'll know. And you'll know what to do next.

Instead of getting frustrated, you'll know how to iterate your way to results

You'll master the art of progressive refinement, building complex outputs and systems, and be able to leverage all of this to be top .5% in your field -- whether that's at work, with a hobby, with a side project, or with something personal.

Instead of being left behind, you'll get and stay ahead of the AI curve

Develop a foundational understanding of how AI & LLMs see, hear, and read your input, + how to use them to 10x your output speed and impact.

Lightning Lessons

Semantic Search with Low-Code