Applied LLMs

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

Jason Liu

Systematically Improving RAG Applications

Staff Analytics Engineer

Systems Engineer

Senior Data Scientist, Product Analytics

ML Engineer

Data Scientist

Individual Consultant

Transform your retrieval from "good enough" to "mission-critical" in weeks, not months Most RAG implementations get stuck in prototype purgatory. They work well for simple cases but fail on complex queries—leading to frustrated users, lost trust, and wasted engineering time. The difference between a prototype and a production-ready system isn't just better technology, it's a fundamentally different mindset. The RAG Implementation RealityWhat you're experiencing right now: ❌ Your RAG demo impressed stakeholders, but real users encounter hallucinations when they need accuracy most❌ Engineers spend countless hours tweaking prompts with minimal improvement❌ Colleagues report finding information manually that your system failed to retrieve❌ You keep making changes but have no way to measure if they're actually helping❌ Every improvement feels like guesswork instead of systematic progress❌ You're unsure which 10% of possible enhancements will deliver 90% of the value What your RAG system could be:With the RAG Flywheel methodology, you'll build a system that: ✅ Retrieves the right information even for complex, ambiguous queries✅ Continuously improves with each user interaction✅ Provides clear metrics to demonstrate ROI to stakeholders✅ Allows your team to make data-driven decisions about improvements✅ Adapts to different content types with specialized capabilities✅ Creates value that compounds over time instead of degrading What Makes This Course DifferentUnlike courses that focus solely on technical implementation, this program gives you the systematic, data-driven approach used by companies to transform prototypes into production systems that deliver real business value: ✅ The Improvement Flywheel: Build synthetic evaluation data that identifies exactly what's failing in your system—even before you have users✅ Fine-tuning Framework: Create custom embedding models with minimal data (as few as 6,000 examples)✅ Feedback Acceleration: Design interfaces that collect 5x more high-quality feedback without annoying users✅ Segmentation System: Analyze user queries to identify which segments need specialized retrievers for 20-40% accuracy gains✅ Multimodal Architecture: Implement specialized indices for different content types (documents, images, tables)✅ Query Routing: Create a unified system that intelligently selects the right retriever for each query The Complete RAG Implementation Framework Week 1: Evaluation SystemsBuild synthetic datasets that pinpoint RAG failures instead of relying on subjective assessments BEFORE: "We need to make the AI better, but we don't know where to start."AFTER: "We know exactly which query types are failing and by how much." Week 2: Fine-tune EmbeddingsCustomize models for 20-40% accuracy gains with minimal examples BEFORE: "Generic embeddings don't understand our domain terminology."AFTER: "Our embedding models understand exactly what 'similar' means in our business context." Week 3: Feedback SystemsDesign interfaces that collect 5x more feedback without annoying users BEFORE: "Users get frustrated waiting for responses and rarely tell us what's wrong."AFTER: "Every interaction provides signals that strengthen our system." Week 4: Query SegmentationIdentify high-impact improvements and prioritize engineering resources BEFORE: "We don't know which features would deliver the most value."AFTER: "We have a clear roadmap based on actual usage patterns and economic impact." Week 5: Specialized SearchBuild specialized indices for different content types that improve retrieval BEFORE: "Our system struggles with anything beyond basic text documents."AFTER: "We can retrieve information from tables, images, and complex documents with high precision." Week 6: Query RoutingImplement intelligent routing that selects optimal retrievers automatically BEFORE: "Different content requires different interfaces, creating a fragmented experience."AFTER: "Users have a seamless experience while the system intelligently routes to specialized components." Real-world Impact From Implementation﻿✅ 85% blueprint image recall: Construction company using visual LLM captioning✅ 90% research report retrieval: Through better text preprocessing techniques✅ $50M revenue increase: Retail company enhancing product search with embedding fine-tuning✅ +14% accuracy boost: Fine-tuning cross-encoders with minimal examples✅ +20% response accuracy: Using re-ranking techniques✅ -30% irrelevant documents: Through improved query segmentation Join 400+ engineers who've transformed their RAG systems with this methodology Your Instructor Jason Liu has built AI systems across diverse domains—from computer vision at the University of Waterloo to content policy at Facebook to recommendation systems at Stitch Fix that boosted revenue by $50 million. His background in managing large-scale data curation, designing multimodal retrieval models, and processing hundreds of millions of recommendations weekly has directly informed his consulting work with companies implementing RAG systems.

Stop building RAG systems that impress in demos but disappoint in production

Course syllabus

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

Billion-Scale Vector Search on Object Storage

Consultant at the intersection of Information Retrieval and AI

Simon Hørup Eskildsen

turbopuffer co-founder

Students will learn to design vector search systems using object storage over traditional disk storage.

Object Storage Vector Search Architecture

Students will learn cost-effective strategies for scaling vector search to petabyte-scale datasets affordably.

Scaling Economics for Petabyte Data

Students will analyze case studies from Cursor, Notion, and Linear to apply vector search design principles.

Real-World Implementation Analysis

Lessons from building verticalized AI agents

Chris Lovejoy

Head of Clinical AI at Anterior

Use domain expert feedback to systematically identify and fix AI failures in vertical applications.

Bridge Data and AI with Failure Mode Taxonomy

Build expert-driven knowledge bases and UIs that enable non-technical users to improve model performance.

Choose Prompting vs Finetuning for Domain AI

Create systems handling sensitive industry data while enabling expert loops and knowledge generation.

Design Secure Vertical AI Architecture

How You Catch Production Hallucinations in Real Time

Julia Neagu

Co-founder & CEO, Quotient AI

Learn to implement monitoring systems that catch AI errors as they happen in live production environments

Build real-time hallucination detectors

Understand how to identify and troubleshoot broken agent behavior using real-world debugging techniques

Debug production AI agent failures

Master adding detection systems to existing AI workflows using just a few lines of practical code

Deploy monitoring with minimal code

Rethinking RAG from first principles for agents

Beyang Liu

CTO and co-founder of Sourcegraph

Students will identify key structural differences between agent and chatbot systems regarding context control.

Agent vs. Chatbot Architecture Differences

Students will design simpler retrieval systems optimized for agents over complex ranking engines.

Agentic RAG Implementation Strategies

Students will apply multi-agent composition patterns to overcome context window limitations.

Multi-Agent System Composition Techniques

Scaling Judge-Time Compute for Robust Auto LLM Evaluation

Leonard Tang

Co-Founder & CEO @ Haize Labs

Identify key failure modes in automated LLM evaluation including bias and inconsistent outputs

LLM Judge Reliability Issues

Apply reasoning model training techniques to improve evaluation reliability and accuracy

Judge-Time Compute Scaling

Implement reinforcement learning methods to build more robust automated assessment tools

RL-Powered Evaluation Systems

Why Devin does not use multi agents

Walden Yan

Co-founder & CPO @ Cognition

Students will identify why multi-agent LLM frameworks fail in practice.

Multi-agent framework limitations

Students will apply foundational context engineering techniques to improve single-agent LLM performance and reliability.

Context engineering principles

Students will evaluate when to choose single-agent vs multi-agent approaches for different LLM application scenarios.

Alternative architecture strategies

Startups to F500: Document Automation Lessons at Scale

Eli Badgio

Co-founder and CTO at Extend

Learn decision frameworks for full automation vs. human-in-the-loop approaches based on error tolerance

When to Automate vs. Augment Human Workflows

Understand how to design testing frameworks that ensure document processing accuracy from project start

Building Robust Evaluation and Validation Systems

Discover strategies for adapting automation solutions from startup environments to enterprise requirements

Scaling Document Automation Across Industries

Lessons on retrieval for autonomous coding agents

Nik Pash

Head of AI @ Cline

Learn how retrieval systems create cognitive overhead that reduces agent performance and focus

Why RAG distracts coding agents

Discover proven frameworks for measuring RAG pipeline effectiveness and ROI impact

Metrics-driven RAG evaluation methods

Identify scenarios where direct code generation outperforms retrieval-augmented approaches

When to skip RAG for coding tasks

Improving retrievers by Reranking and embedding fine-tuning

Ayush Chaurasia

ML Engineer @ LanceDB

Master reranker architecture and implementation to effectively boost retrieval accuracy beyond embedding models alone.

Reranking Fundamentals

Apply practical criteria for deciding when to fine-tune embeddings vs. implementing rerankers based on use case demands.

Strategic Model Fine-tuning

Evaluate deployment tradeoffs to select optimal reranking and embedding approaches for various hardware environments.

Performance Optimization

How to Build Self-Improving AI via Data-Driven Loops

Hamish Ogilvy

VP of AI, Algolia

Students will learn to build flexible AI query planners that adapt execution strategies in real-time.

Design Dynamic Query Planners

Students will understand how to develop modular search components that can be dynamically assembled by AI systems.

Create Composable Search Primitives

Students will learn to connect search systems to data feedback for continuous improvement.

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