DSPy: Learn how to program (not prompt) language models
DSPy: Learn how to program (not prompt) language models
Published 6/2024
Duration: 3h18m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 1.64 GB
Genre: eLearning | Language: English
From Manual Prompting to Automated Mastery: Building Resilient Systems with DSPy
What you'll learn
Learn the fundamental abstractions of DSPy (signatures, modules, language models, retrieval models, optimisers, metrics, and assertions)
Learn how to build a RAG (Retrieval Augmented Generation) system using DSPy
Build a stock analyst using DSPy
Learn synthetic prompt optimisation using DSPy and LangChain
Requirements
Basic Python Knowledge
Description
In this tutorial, I will introduce you to DSPy, a powerful framework for programming (not prompting) language models. We will explore how DSPy can help you build LLM applications by reducing the need for manual prompt optimization. I will explain the concept of signatures and modules, and how they can be used to define the input, outputs, and flow of your application. Additionally, I will demonstrate how DSPy allows for prompt optimization using data sets. Throughout the tutorial, we will work on practical coding examples, including building a stock analysis tool, a chess playing agent, and BabyAGI, an autonomous task-based AI agent. Join me to learn more about DSPy and its exciting features!
This course is specifically designed for two main groups of learners:
Python Developers Eager to Learn About AI and Prompt Optimization
:
If you have a solid foundation in Python programming and are looking to expand your expertise into the exciting field of artificial intelligence, this course will provide you with the practical skills and theoretical knowledge necessary to excel. This course is ideal for developers who are interested in understanding how to leverage AI for automated processes, specifically in optimizing prompts to interact effectively with AI models. Whether you aim to enhance your current projects or want to explore new opportunities in AI, this course will equip you with cutting-edge skills in AI application development.
AI Engineers Tired of Manual Prompt Optimization
:
For AI professionals who find the task of manual prompt optimization tedious and time-consuming, this course offers a deep dive into automated solutions. Learn how to implement advanced techniques that streamline your workflow and improve the efficiency and effectiveness of your AI systems. This course will teach you how to use Python to automate and refine the process of prompt optimization, enabling you to focus on more strategic aspects of AI engineering.
What Will You Gain?
By the end of this course, learners will be able to:
Understand the principles and methodologies behind AI and prompt optimization.
Apply Python programming skills to automate and optimize prompts for AI models.
Enhance AI system efficiencies by reducing the need for manual input adjustments.
Innovate within their projects or roles by incorporating advanced AI techniques.
Who this course is for:
Python Developers Eager to Learn About AI and Prompt Optimization
AI Engineers Tired of Manual Prompt Optimization
More Info
What you'll learn
Learn the fundamental abstractions of DSPy (signatures, modules, language models, retrieval models, optimisers, metrics, and assertions)
Learn how to build a RAG (Retrieval Augmented Generation) system using DSPy
Build a stock analyst using DSPy
Learn synthetic prompt optimisation using DSPy and LangChain
Requirements
Basic Python Knowledge
Description
In this tutorial, I will introduce you to DSPy, a powerful framework for programming (not prompting) language models. We will explore how DSPy can help you build LLM applications by reducing the need for manual prompt optimization. I will explain the concept of signatures and modules, and how they can be used to define the input, outputs, and flow of your application. Additionally, I will demonstrate how DSPy allows for prompt optimization using data sets. Throughout the tutorial, we will work on practical coding examples, including building a stock analysis tool, a chess playing agent, and BabyAGI, an autonomous task-based AI agent. Join me to learn more about DSPy and its exciting features!
This course is specifically designed for two main groups of learners:
Python Developers Eager to Learn About AI and Prompt Optimization
:
If you have a solid foundation in Python programming and are looking to expand your expertise into the exciting field of artificial intelligence, this course will provide you with the practical skills and theoretical knowledge necessary to excel. This course is ideal for developers who are interested in understanding how to leverage AI for automated processes, specifically in optimizing prompts to interact effectively with AI models. Whether you aim to enhance your current projects or want to explore new opportunities in AI, this course will equip you with cutting-edge skills in AI application development.
AI Engineers Tired of Manual Prompt Optimization
:
For AI professionals who find the task of manual prompt optimization tedious and time-consuming, this course offers a deep dive into automated solutions. Learn how to implement advanced techniques that streamline your workflow and improve the efficiency and effectiveness of your AI systems. This course will teach you how to use Python to automate and refine the process of prompt optimization, enabling you to focus on more strategic aspects of AI engineering.
What Will You Gain?
By the end of this course, learners will be able to:
Understand the principles and methodologies behind AI and prompt optimization.
Apply Python programming skills to automate and optimize prompts for AI models.
Enhance AI system efficiencies by reducing the need for manual input adjustments.
Innovate within their projects or roles by incorporating advanced AI techniques.
Who this course is for:
Python Developers Eager to Learn About AI and Prompt Optimization
AI Engineers Tired of Manual Prompt Optimization
More Info
