CF

Systematically Improving RAG Applications - Bonus Content

24h 50m 24s
English
Paid

Systematically Improving RAG Applications: Bonus Content - Enrich your learning experience with access to exceptional materials from prior cohorts, including workshops, guest lectures, and Q&A sessions. Dive into a wealth of practical cases, best practices, and in-depth technical analyses.

Cohort 1

Workshops

Engage in six practical workshops from the first cohort that complement the main program:

  • Workshop 1
  • Workshop 2
  • Workshop 3
  • Workshop 4
  • Workshop 5
  • Workshop 6

Guest Lectures

Listen to insightful lectures from experts at leading AI companies:

  • Building Dynamic AI Memory Systems - Personalization by Sam Whitmore
  • Text Chunking in RAG - Insights by Anton from ChromaDB
  • Custom RAG Evaluations - With Vespa.ai
  • Multimodal RAG, Hybrid Search & Re-ranking Tips - With LanceDB and Unstructured
  • Leveraging User Feedback - Experiences from Zapier Central
  • Guide to RAG Complexity - Featured by Cohere and Modal Labs
  • Boosting BM25 with Generative AI - Doug Turnbull

Office Hours

Engage with experts through a series of Q&A sessions addressing practical challenges such as:

  • Building scalable RAG systems
  • Query optimization and routing
  • Fine-tuning re-rankers
  • Exploring alternative search methods: ColBERT, SPLADE
  • Implementing DAGs and serverless architecture
  • Specialized sessions for the APAC region

Cohort 2

Guest Lectures

New insights from the second cohort's guest expert sessions:

  • Evolving workflows for RAG optimization
  • Lexical Love - Shared by John Berriman
  • Common Mistakes with Evals - Clarified by Hamel Husain
  • Workflow Agents - A deep dive by Jerry Liu (LlamaIndex)
  • Query Routing - Perspective by Anton Troynikov (ChromaDB)
  • Fine-Tuning for Enterprise Search - Insights from the Glean team

About the Author: Jason Liu

Jason Liu thumbnail

Jason Liu is a US ML engineer and the creator of Instructor (the most-used Python library for getting structured outputs from LLMs) and a long-running independent voice on the production-engineering side of LLM applications. He consults with companies on RAG implementations and is widely cited for the rigour of his approach to systematic RAG improvement.

His CourseFlix listing carries three Jason Liu courses: Systematically Improving RAG Applications, the accompanying Bonus Content module, and 3 Day AI Coding Accelerator. The RAG material is unusual for the depth it goes into the eval and feedback-loop side of production RAG systems — the parts of RAG work that separate a working RAG pipeline from one that hallucinates.

Material is paid and aimed at engineers running RAG in production who want to make the system measurably better rather than relying on prompt-engineering by intuition. For broader content, see CourseFlix's RAG category page.

Watch Online 28 lessons

This is a demo lesson (10:00 remaining)

You can watch up to 10 minutes for free. Subscribe to unlock all 28 lessons in this course and access 10,000+ hours of premium content across all courses.

View Pricing
0:00
/
#1: Cohort 1 Guest Lectures Building Dynamic AI Memory Systems Sam Whitmore's Approach to Personalization
All Course Lessons (28)
#Lesson TitleDurationAccess
1
Cohort 1 Guest Lectures Building Dynamic AI Memory Systems Sam Whitmore's Approach to Personalization Demo
55:45
2
Cohort 1 Guest Lectures Text Chunking in RAG Essential Guide with Anton from ChromaDB
01:01:30
3
Cohort 1 Guest Lectures Custom RAG Evaluations w Vespa.ai
53:38
4
Cohort 1 Guest Lectures Multimodal RAG, Hybrid Search & Re-ranking Tips with LanceDB and Unstructured
59:05
5
Cohort 1 Guest Lectures Leveraging User Feedback for Better RAG Systems Lessons from Zapier Central
55:17
6
Cohort 1 Guest Lectures Cohere's Guide to RAG Complexity and Tips from Modal Labs
01:08:24
7
Cohort 1 Guest Lectures Boosting BM25 with Generative AI Insights from Doug Turnbull
58:12
8
Cohort 1 Office Hours Beyond Dense Embeddings Exploring Colbert, SPLADE, & Advanced Retrieval Techniques Office Hours
25:32
9
Cohort 1 Office Hours Building Resilient RAG Systems for Large-Scale Data Office Hours
01:00:09
10
Cohort 1 Office Hours Building Scalable Systems with DAGs and Serverless for RAG APAC Office Hours
59:01
11
Cohort 1 Office Hours Data Flywheels & Fine-Tuning Re-Rankers for Retrieval Systems Office Hours
29:17
12
Cohort 1 Office Hours Evaluating Agent Performance and Planning Reliability in AI Systems APAC Office Hours
47:42
13
Cohort 1 Office Hours Handling Real-Time Insights & Advanced Retrieval Challenges APAC Office Hours
51:37
14
Cohort 1 Office Hours Optimizing Planner and Feedback Mechanisms in RAG Systems APAC Office Hours
44:25
15
Cohort 1 Office Hours Smart Routing and Query Optimization for Advanced Retrieval Systems Office Hours
42:23
16
Cohort 1 Office Hours Using AI to Streamline Query Understanding & User Feedback Office Hours
44:33
17
Cohort 2 Guest Lectures Building document workflow agents with Jerry Liu from Llama Index
44:28
18
Cohort 2 Guest Lectures Building scalable RAG applications Evolving workflows for sharing and optimization [Ankur Goyal]
45:20
19
Cohort 2 Guest Lectures Common Mistakes People Make with Evals [Hamel Husain]
40:34
20
Cohort 2 Guest Lectures Inside Glean Fine-Tuning Embedding Models for Optimized AI and Enterprise Search
47:25
21
Cohort 2 Guest Lectures Lexical Love Rediscovering the Power of Text in RAG [John Berryman]
46:28
22
Cohort 2 Guest Lectures Organizing Your Data for Query Routing [Anton Troynikov from ChromaDB]
44:55
23
RAG Cohort 1 - Workshop 1
01:00:00
24
RAG Cohort 1 - Workshop 2
01:38:02
25
RAG Cohort 1 - Workshop 3
01:05:01
26
RAG Cohort 1 - Workshop 4
58:20
27
RAG Cohort 1 - Workshop 5
57:37
28
RAG Cohort 1 - Workshop 6
01:05:44
Unlock unlimited learning

Get instant access to all 27 lessons in this course, plus thousands of other premium courses. One subscription, unlimited knowledge.

Learn more about subscription

Related courses

  • RAG: Beyond Basics thumbnailUpdated 7mo ago

    RAG: Beyond Basics

    By: Prompt Engineering
    Explore the cutting-edge world of Retrieval-Augmented Generation (RAG) in this comprehensive course designed to deepen your understanding of both the.
    2h 40m5/5
  • RAG for Real-World AI Applications thumbnailUpdated 6mo ago

    RAG for Real-World AI Applications

    By: Vue School, Justin Schroeder, Daniel Kelly, Garrison Snelling
    Study the RAG approach to enhance AI with your own data. Learn about vectors, embeddings, and integration. Apply the approach in real projects.
    26m
  • RAG (Retrieval) thumbnailUpdated 11mo ago

    RAG (Retrieval)

    By: Mckay Wrigley
    Unlock the potential of Retrieval-Augmented Generation (RAG) systems with our comprehensive course.
    4h 33m5/5

Frequently asked questions

What is Systematically Improving RAG Applications - Bonus Content about?
Systematically Improving RAG Applications: Bonus Content - Enrich your learning experience with access to exceptional materials from prior cohorts, including workshops , guest lectures , and Q&A sessions . Dive into a wealth of practical…
Who teaches this course?
It is taught by Jason Liu. You can find more courses by this instructor on the corresponding source page.
How long is the course?
It contains 28 lessons with a total runtime of 24 hours 50 minutes. Every lesson is available to watch online at your own pace.
Is it free to watch?
It is part of CourseFlix's premium catalog. A subscription unlocks the full video player; the course description, table of contents, and preview information are available to everyone.
Where can I watch it online?
The course is available to watch online on CourseFlix at https://courseflix.net/course/systematically-improving-rag-applications-bonus-content. The page hosts every lesson with the integrated video player; no download is required.