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# Tutorial: DSPy
DSPy helps you build and optimize *programs* that use **Language Models (LMs)** and **Retrieval Models (RMs)**.
Think of it like composing Lego bricks (**Modules**) where each brick performs a specific task (like generating text or retrieving information).
**Signatures** define what each Module does (its inputs and outputs), and **Teleprompters** automatically tune these modules (like optimizing prompts or examples) to get the best performance on your data.
**Source Repository:** [https://github.com/stanfordnlp/dspy/tree/7cdfe988e6404289b896d946d957f17bb4d9129b/dspy](https://github.com/stanfordnlp/dspy/tree/7cdfe988e6404289b896d946d957f17bb4d9129b/dspy)
```mermaid
flowchart TD
A0["Module / Program"]
A1["Signature"]
A2["Predict"]
A3["LM (Language Model Client)"]
A4["RM (Retrieval Model Client)"]
A5["Teleprompter / Optimizer"]
A6["Example"]
A7["Evaluate"]
A8["Adapter"]
A9["Settings"]
A0 -- "Contains / Composes" --> A0
A0 -- "Uses (via Retrieve)" --> A4
A1 -- "Defines structure for" --> A6
A2 -- "Implements" --> A1
A2 -- "Calls" --> A3
A2 -- "Uses demos from" --> A6
A2 -- "Formats prompts using" --> A8
A5 -- "Optimizes" --> A0
A5 -- "Fine-tunes" --> A3
A5 -- "Uses training data from" --> A6
A5 -- "Uses metric from" --> A7
A7 -- "Tests" --> A0
A7 -- "Evaluates on dataset of" --> A6
A8 -- "Translates" --> A1
A8 -- "Formats demos from" --> A6
A9 -- "Configures default" --> A3
A9 -- "Configures default" --> A4
A9 -- "Configures default" --> A8
```
## Chapters
1. [Module / Program](01_module___program.md)
2. [Signature](02_signature.md)
3. [Example](03_example.md)
4. [Predict](04_predict.md)
5. [LM (Language Model Client)](05_lm__language_model_client_.md)
6. [RM (Retrieval Model Client)](06_rm__retrieval_model_client_.md)
7. [Evaluate](07_evaluate.md)
8. [Teleprompter / Optimizer](08_teleprompter___optimizer.md)
9. [Adapter](09_adapter.md)
10. [Settings](10_settings.md)
---
Generated by [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)