Adding Depth to DSPy Programs

Henry AI Labs · Beginner ·🔍 RAG & Vector Search ·2y ago
Hey everyone! Thank you so much for watching the 3rd edition of the DSPy series, Adding Depth to DSPy Programs!! This video begins with some DSPy news such as STORM, DSPy Assertions, and Typed Signatures! We then dive into the concept of adding depth to DSPy programs, taking a further look at what it means to have unique input-output examples for each component and how we can compose DSPy programs with different LLMs per component! We then dive into two notebooks illustrating adding depth to RAG programs and a 4-layer question to blog post writer! Demo #1 Notebook: https://github.com/weaviate/recipes/blob/main/integrations/llm-frameworks/dspy/3.Adding-Depth-to-RAG-Programs.ipynb Demo #2 Notebook: https://github.com/weaviate/recipes/blob/main/integrations/llm-frameworks/dspy/2.Writing-Blog-Posts-with-DSPy.ipynb You can find the examples and links to community resources / news on https://github.com/weaviate/recipes! Chapters 0:00 Intro 0:50 Chapters Overview 5:06 Weaviate Recipes 5:24 DSPy News and Community Notes 13:51 Adding Depth to RAG Programs 18:40 Multi-Model DSPy Programs 20:18 DSPy Optimizers 25:30 Deep Dive Optimizers 27:55 Into the Optimizer Code! 37:48 Demo #1: Adding Depth to RAG 1:05:25 Demo #2: Questions to Blogs 1:07:48 Thank you so much for watching!
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from Connor Shorten · Connor Shorten · 0 of 60

← Previous Next →
1 DenseNets
DenseNets
Connor Shorten
2 DeepWalk Explained
DeepWalk Explained
Connor Shorten
3 Inception Network Explained
Inception Network Explained
Connor Shorten
4 StackGAN
StackGAN
Connor Shorten
5 StyleGAN
StyleGAN
Connor Shorten
6 Progressive Growing of GANs Explained
Progressive Growing of GANs Explained
Connor Shorten
7 Improved Techniques for Training GANs
Improved Techniques for Training GANs
Connor Shorten
8 Word2Vec Explained
Word2Vec Explained
Connor Shorten
9 Must Read Papers on GANs
Must Read Papers on GANs
Connor Shorten
10 Unsupervised Feature Learning
Unsupervised Feature Learning
Connor Shorten
11 Self-Supervised GANs
Self-Supervised GANs
Connor Shorten
12 Embedding Graphs with Deep Learning
Embedding Graphs with Deep Learning
Connor Shorten
13 Transfer Learning in GANs
Transfer Learning in GANs
Connor Shorten
14 ReLU Activation Function
ReLU Activation Function
Connor Shorten
15 AC-GAN Explained
AC-GAN Explained
Connor Shorten
16 SimGAN Explained
SimGAN Explained
Connor Shorten
17 DC-GAN Explained!
DC-GAN Explained!
Connor Shorten
18 ResNet Explained!
ResNet Explained!
Connor Shorten
19 Graph Convolutional Networks
Graph Convolutional Networks
Connor Shorten
20 Neural Architecture Search
Neural Architecture Search
Connor Shorten
21 Henry AI Labs
Henry AI Labs
Connor Shorten
22 Video Classification with Deep Learning
Video Classification with Deep Learning
Connor Shorten
23 BigGANs in Data Augmentation
BigGANs in Data Augmentation
Connor Shorten
24 Introduction to Deep Learning
Introduction to Deep Learning
Connor Shorten
25 EfficientNet Explained!
EfficientNet Explained!
Connor Shorten
26 Self-Attention GAN
Self-Attention GAN
Connor Shorten
27 Curriculum Learning in Deep Neural Networks
Curriculum Learning in Deep Neural Networks
Connor Shorten
28 Deep Learning Podcast #1 | Edward Dixon | Stochastic Weight Averaging
Deep Learning Podcast #1 | Edward Dixon | Stochastic Weight Averaging
Connor Shorten
29 Deep Compression
Deep Compression
Connor Shorten
30 Skin Cancer Classification with Deep Learning
Skin Cancer Classification with Deep Learning
Connor Shorten
31 Deep Learning Podcast #2 | Edward Peake | Deep Learning in Medical Imaging
Deep Learning Podcast #2 | Edward Peake | Deep Learning in Medical Imaging
Connor Shorten
32 The Lottery Ticket Hypothesis Explained!
The Lottery Ticket Hypothesis Explained!
Connor Shorten
33 SqueezeNet
SqueezeNet
Connor Shorten
34 GauGAN Explained!
GauGAN Explained!
Connor Shorten
35 AutoML with Hyperband
AutoML with Hyperband
Connor Shorten
36 DL Podcast #3 | Yannic Kilcher | Population-Based Search
DL Podcast #3 | Yannic Kilcher | Population-Based Search
Connor Shorten
37 Weakly Supervised Pretraining
Weakly Supervised Pretraining
Connor Shorten
38 Image Data Augmentation for Deep Learning
Image Data Augmentation for Deep Learning
Connor Shorten
39 Unsupervised Data Augmentation
Unsupervised Data Augmentation
Connor Shorten
40 Wide ResNet Explained!
Wide ResNet Explained!
Connor Shorten
41 RevNet: Backpropagation without Storing Activations
RevNet: Backpropagation without Storing Activations
Connor Shorten
42 GANs with Fewer Labels
GANs with Fewer Labels
Connor Shorten
43 BigBiGAN Unsupervised Learning!
BigBiGAN Unsupervised Learning!
Connor Shorten
44 Self-Supervised Learning
Self-Supervised Learning
Connor Shorten
45 Multi-Task Self-Supervised Learning
Multi-Task Self-Supervised Learning
Connor Shorten
46 Self-Supervised GANs
Self-Supervised GANs
Connor Shorten
47 Population Based Training
Population Based Training
Connor Shorten
48 Show, Attend and Tell
Show, Attend and Tell
Connor Shorten
49 Siamese Neural Networks
Siamese Neural Networks
Connor Shorten
50 WaveGAN Explained!
WaveGAN Explained!
Connor Shorten
51 VAE-GAN Explained!
VAE-GAN Explained!
Connor Shorten
52 Evolution in Neural Architecture Search!
Evolution in Neural Architecture Search!
Connor Shorten
53 AI Research Weekly Update August 18th, 2019
AI Research Weekly Update August 18th, 2019
Connor Shorten
54 Weight Agnostic Neural Networks Explained!
Weight Agnostic Neural Networks Explained!
Connor Shorten
55 AI Research Weekly Update August 25th, 2019
AI Research Weekly Update August 25th, 2019
Connor Shorten
56 Neuroevolution of Augmenting Topologies (NEAT)
Neuroevolution of Augmenting Topologies (NEAT)
Connor Shorten
57 CoDeepNEAT
CoDeepNEAT
Connor Shorten
58 AI Research Weekly Update September 1st, 2019
AI Research Weekly Update September 1st, 2019
Connor Shorten
59 Randomly Wired Neural Networks
Randomly Wired Neural Networks
Connor Shorten
60 Genetic CNN
Genetic CNN
Connor Shorten

Related AI Lessons

Chunking Is Easy. Parsing Is Hard.
Learn why your RAG pipeline may be reasoning over broken data and how to improve it by understanding the differences between chunking and parsing
Medium · AI
Chunking Is Easy. Parsing Is Hard.
Learn how chunking and parsing impact RAG pipelines and why parsing is a crucial step in ensuring high-quality data
Medium · Machine Learning
RAG Evaluation with RAGAS: Measuring Faithfulness, Context Precision, and Recall in Production
Learn to evaluate RAG models using RAGAS, measuring faithfulness, context precision, and recall in production environments
Dev.to · Anna Danilec
Chunking for RAG: stop tuning the wrong knob
Learn how to optimize RAG performance with a practical chunking playbook, avoiding common pitfalls and improving evaluation metrics
Dev.to · saurabh naik

Chapters (12)

Intro
0:50 Chapters Overview
5:06 Weaviate Recipes
5:24 DSPy News and Community Notes
13:51 Adding Depth to RAG Programs
18:40 Multi-Model DSPy Programs
20:18 DSPy Optimizers
25:30 Deep Dive Optimizers
27:55 Into the Optimizer Code!
37:48 Demo #1: Adding Depth to RAG
1:05:25 Demo #2: Questions to Blogs
1:07:48 Thank you so much for watching!
Up next
Watch this before applying for jobs as a developer.
Tech With Tim
Watch →