| Beginner |
Getting Started |
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Introduces the basic building blocks in DSPy. Tackles the task of complex question answering with HotPotQA. |
| Beginner |
Minimal Working Example |
N/A |
Builds and optimizes a very simple chain-of-thought program in DSPy for math question answering. Very short. |
| Beginner |
Compiling for Tricky Tasks |
N/A |
Teaches LMs to reason about logical statements and negation. Uses GPT-4 to bootstrap few-shot CoT demonstrations for GPT-3.5. Establishes a state-of-the-art result on ScoNe. Contributed by Chris Potts. |
| Beginner |
Local Models & Custom Datasets |
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Illustrates two different things together: how to use local models (Llama-2-13B in particular) and how to use your own data examples for training and development. |
| Intermediate |
The DSPy Paper |
N/A |
Sections 3, 5, 6, and 7 of the DSPy paper can be consumed as a tutorial. They include explained code snippets, results, and discussions of the abstractions and API. |
| Intermediate |
DSPy Assertions |
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Introduces example of applying DSPy Assertions while generating long-form responses to questions with citations. Presents comparative evaluation in both zero-shot and compiled settings. |
| Intermediate |
Finetuning for Complex Programs |
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Teaches a local T5 model (770M) to do exceptionally well on HotPotQA. Uses only 200 labeled answers. Uses no hand-written prompts, no calls to OpenAI, and no labels for retrieval or reasoning. |
| Advanced |
Information Extraction |
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Tackles extracting information from long articles (biomedical research papers). Combines in-context learning and retrieval to set SOTA on BioDEX. Contributed by Karel D’Oosterlinck. |