Master DSPy, Stanford NLP's LLM Programming framework, through a comprehensive 35-minute video tutorial that explores 8 practical examples demonstrating how to build complex Language Model programs efficiently. Progress from basic concepts to advanced implementations, covering prompt programming fundamentals, chain of thought reasoning, data type predictions, Retrieval Augmented Generation (RAG), multi-hop processing, optimizer usage, few-shot prompting, dataset generation, and model fine-tuning. Learn to work with various LLMs including ChatGPT, Mixtral, Gemini, and LLama-3, as well as local models like T5, LLama, and GPT-1. Access supplementary materials including write-ups, slides, and notebooks through the provided Patreon link, and explore additional resources through the official DSPy documentation and GitHub repository.
DSPy Tutorial - Programming Language Models with 8 Key Examples
Neural Breakdown with AVB via YouTube
Overview
Syllabus
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- Intro
- Prompt Programming vs Engineering
- Example 1 - Basic QA
- Example 2 - Chain of Thought
- Example 3 - Predicting floats, bools, JSON
- Example 4 - Retrieval Augmented Generation RAG
- Example 5 - Multi Hop
- Example 6 - Optimizers and Few Shot Prompts
- Example 6b - Assert and Suggest
- Example 7 - Generating Datasets
- Example 8 - Finetune a T5 model with ChatGPT
- Outro
Taught by
Neural Breakdown with AVB
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