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Fix redundant links to DSPy Optimizers page #2136

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2530257
Fix redundant links to DSPy Optimizers page
mdlinville Feb 9, 2026
07de0bc
Fix broken link from bad search/replace
mdlinville Feb 9, 2026
9e18706
Fix Weave link
mdlinville Feb 9, 2026
18a14f2
Merge remote-tracking branch 'origin/mdlinville-patch-1' into mdlinvi…
mdlinville Feb 9, 2026
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8 changes: 4 additions & 4 deletions weave/cookbooks/dspy_prompt_optimization.mdx
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Expand Up @@ -20,7 +20,7 @@ This tutorial demonstrates how we can improve the performance of our LLM workflo
We need the following libraries for this tutorial:

- [DSPy](https://dspy.ai) for building the LLM workflow and optimizing it.
- [Weave](/weave/quickstart) to track our LLM workflow and evaluate our prompting strategies.
- [Weave](/weave) to track our LLM workflow and evaluate our prompting strategies.
- [datasets](https://huggingface.co/docs/datasets/index) to access the Big-Bench Hard dataset from HuggingFace Hub.

```python lines
Expand Down Expand Up @@ -117,7 +117,7 @@ dspy_train_examples, dspy_val_examples = get_dataset(metadata)

[DSPy](https://dspy.ai) is a framework that pushes building new LM pipelines away from manipulating free-form strings and closer to programming (composing modular operators to build text transformation graphs) where a compiler automatically generates optimized LM invocation strategies and prompts from a program.

We will use the [`dspy.OpenAI`](https://dspy.ailearn/programming/language_models/#__tabbed_1_1) abstraction to make LLM calls to [GPT3.5 Turbo](https://platform.openai.com/docs/models/gpt-3.5-turbo).
We will use the [`dspy.OpenAI`](https://dspy.ai/learn/programming/language_models/#__tabbed_1_1) abstraction to make LLM calls to [GPT3.5 Turbo](https://platform.openai.com/docs/models/gpt-3.5-turbo).

```python lines
system_prompt = """
Expand All @@ -131,7 +131,7 @@ dspy.settings.configure(lm=llm)

### Writing the Causal Reasoning Signature

A [signature](https://dspy.ailearn/programming/signatures) is a declarative specification of input/output behavior of a [DSPy module](https://dspy.ailearn/programming/modules) which are task-adaptive components—akin to neural network layers—that abstract any particular text transformation.
A [signature](https://dspy.ai/learn/programming/signatures) is a declarative specification of input/output behavior of a [DSPy module](https://dspy.ai/learn/programming/modules) which are task-adaptive components—akin to neural network layers—that abstract any particular text transformation.

```python lines
from pydantic import BaseModel, Field
Expand Down Expand Up @@ -220,7 +220,7 @@ Running the evaluation causal reasoning dataset will cost approximately $0.24 in

## Optimizing our DSPy Program

Now, that we have a baseline DSPy program, let us try to improve its performance for causal reasoning using a [DSPy teleprompter](https://dspy.ailearn/optimization/optimizers/) that can tune the parameters of a DSPy program to maximize the specified metrics. In this tutorial, we use the [BootstrapFewShot](https://dspy.aiapi/optimizers/BootstrapFewShot/) teleprompter.
Now, that we have a baseline DSPy program, let us try to improve its performance for causal reasoning using the [BootstrapFewShot](https://dspy.ai/api/optimizers/BootstrapFewShot/) teleprompter, which can tune the parameters of a DSPy program to maximize the specified metrics.

```python lines
from dspy.teleprompt import BootstrapFewShot
Expand Down