Stop Prompt Engineering! Program Your LLMs with DSPy

Can algorithmically optimizing prompts outperform prompt engineering? Resources: DSPy Breakdown: https://github.com/ALucek/dspy-breakdown/blob/main/dspy_breakdown.ipynb DSPy Documentation: https://dspy.ai/ DSPy Repo: https://github.com/stanfordnlp/dspy Chapters: 00:00 - What is DSPy? 02:51 - Setup 04:12 - Signatures Overview 10:41 - Modules Overview 12:01 - Modules: Chain of Thought 13:04 - Modules: Program of Thought 14:53 - Modules: ReAct 17:47 - Modules: Others 19:22 - Why We Need Metrics 20:49 - Metrics: Example Data Type & Overview 23:07 - Metrics: Simple 24:46 - Metrics: Intermediate 27:21 - Metrics: Advanced 30:01 - Optimizers Overview 33:20 - Optimizer: Labeled Few Shot 34:45 - Optimizer: Bootstrap Few Shot 36:48 - Optimizer: Bootstrap Few Shot With Random Search 38:49 - Optimizer: KNN Fewshot 40:07 - Optimizer: COPRO 43:01 - Optimizer: MIPROv2 45:28 - Fine Tuning Based Optimizers 46:10 - Optimizer: Bootstrap Fine Tune 49:06 - Choosing Optimizers 50:05 - Optimizing Optimizers 51:17 - Is DSPy Worth it? #ai #DSPy #datascience