Dspy
orchestra-research/ai-research-skills/16-prompt-engineering/dspyby orchestra-research773a52944ba4MIT13K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 3 months ago
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
Only the file list is public. File contents are available once the skill is installed in a workspace.
| Path | Size | Type |
|---|---|---|
| references/examples.md | 18.5 KB | text/markdown |
| references/modules.md | 12.1 KB | text/markdown |
| references/optimizers.md | 14.6 KB | text/markdown |
| SKILL.md | 14.9 KB | text/markdown |
Source and attribution
Source:orchestra-research/ai-research-skillsin16-prompt-engineering/dspyat commit773a529
License: MIT
Content belongs to its original authors. SourceWeft indexes it from a public repository.
More from orchestra-research/ai-research-skills
Speculative Decoding
orchestra-research
Guides accelerating LLM inference with speculative decoding, Medusa multi-head prediction, and lookahead decoding.
Systems Paper Writing
orchestra-research
Guides paragraph-level structure and page budgets for writing systems conference papers such as OSDI, SOSP, ASPLOS and NSDI.
Moe Training
orchestra-research
Guides training and inference of Mixture of Experts models with DeepSpeed or HuggingFace, covering routing, load balancing and expert parallelism.
Presenting Conference Talks
orchestra-research
Generates conference presentation slides (Beamer LaTeX PDF and editable PPTX) from a compiled paper with speaker notes and talk script. Use when preparing oral talks, spotlight presentations, or invited talks for ML and systems conferences.
Model Pruning
orchestra-research
Guides LLM pruning with Wanda, SparseGPT, magnitude and N:M methods to cut model size and speed inference.
Outlines
orchestra-research
Guides use of the Outlines library for structured, schema-constrained text generation with local models.