Updates to examples/tweets/compiling_langchain.ipynb

okhat · okhat · commit e00bc8a1fb1c · 2024-01-09T21:17:21.000Z
diff --git a/examples/tweets/compiling_langchain.ipynb b/examples/tweets/compiling_langchain.ipynb
@@ -13,7 +13,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One of the most powerful features in **DSPy** are its optimizers. **DSPy optimizers** can take any LM system and tune the prompts (or the LM weights) to maximize any objective.\n",
+    "One of the most powerful features in **DSPy** is optimizers. **DSPy optimizers** can take any LM system and tune the prompts (or the LM weights) to maximize any objective.\n",
     "\n",
     "Optimizers can improve the quality of your LM systems and make your code adaptive to new LMs or new data. This is meant to bring structure and modularity in place of hacky things like (i) manual prompt engineering, (ii) designing complex pipelines for generating synthetic data, (iii) or designing complex pipelines for finetuning."
    ]

Original file line number	Diff line number	Diff line change
`@@ -13,7 +13,7 @@`
`13`	`13`	`"cell_type": "markdown",`
`14`	`14`	`"metadata": {},`
`15`	`15`	`"source": [`
`16`		`- "One of the most powerful features in DSPy are its optimizers. DSPy optimizers can take any LM system and tune the prompts (or the LM weights) to maximize any objective.\n",`
	`16`	`+ "One of the most powerful features in DSPy is optimizers. DSPy optimizers can take any LM system and tune the prompts (or the LM weights) to maximize any objective.\n",`
`17`	`17`	`"\n",`
`18`	`18`	`"Optimizers can improve the quality of your LM systems and make your code adaptive to new LMs or new data. This is meant to bring structure and modularity in place of hacky things like (i) manual prompt engineering, (ii) designing complex pipelines for generating synthetic data, (iii) or designing complex pipelines for finetuning."`
`19`	`19`	`]`