Can You Tell Me About DSPy: How DSPy Automates Prompt Engineering Modularly
Demonstrate-Search-Predict (DSP), with 'Py'thon
Developed by Stanford NLP Group
DSPy provides general purpose modules that replace string-based
prompting. DSPy also provides optimizers which can optimize these
modules with help of following
What are the various modules DSPy has?
Lang Models, Signatures, Modules, Data, Metrics, Optimizers, Assertions
Can you talk about use cases of DSPy?
QA, Classification, Summarisatio, RAGs / Multi-Hop rags,
Reasoning
But can you tell me Why DSPy?
Streamline the process of prompt-engineering with input and objective
Create Modules of LMs that can work to achieve objective
Then train the modules by using Datasets built by hand, and with LLMs
Evalute the modules with the Metrics that is custom coded in DSPy
Including Fine-tuning smaller models like T5 and Bert
Chapter Navigation:
0:00 Intro
0:50 Video Overview
2:00 DSPy Way
3:10 What DSPy does
5:25 Modules in DSPy
6:10 Tasks Done in DSPy
6:50 Installing DSPy-AI
7:50 Use Virtual Environment
9:10 Minimal Example of DSPY
10:10 Creating & Prompting LM object
12:50 Creating Predict Object
14:20 TypeError in Predict
15:00 Prediction Object
16:15 Reviewing LM Calls History
16:40 Reviewing Prompt Signature
17:35 Programming Prompts
18:00 How Prompts are Programmed
18:45 What is Next
19:25 Why DSPy?
20:30 Outro
The data and the code is located at
https://github.com/insightbuilder/pyt...
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