chore: import upstream snapshot with attribution

2026-07-13 12:48:47 +08:00
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+---
+layout: default
+title: "DSPy"
+nav_order: 9
+has_children: true
+---
+
+# Tutorial: DSPy
+
+> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+DSPy<sup>[View Repo](https://github.com/stanfordnlp/dspy/tree/7cdfe988e6404289b896d946d957f17bb4d9129b/dspy)</sup> 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.
+
+```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
+```