diff --git a/prompt_evaluations/05_prompt_foo_code_graded_animals/lesson.ipynb b/prompt_evaluations/05_prompt_foo_code_graded_animals/lesson.ipynb
index d27398c..33a94ab 100644
--- a/prompt_evaluations/05_prompt_foo_code_graded_animals/lesson.ipynb
+++ b/prompt_evaluations/05_prompt_foo_code_graded_animals/lesson.ipynb
@@ -196,7 +196,7 @@
     "\n",
     "The next step is telling promptfoo about the particular tests we'd like to run with our specific prompts and providers.  Promptfoo gives us many options for how we define our tests, but we'll start with one of the most common approaches: specifying our tests inside a CSV file.\n",
     "\n",
-    "We'll make a new CSV file called `dataset.csv` and write our test inputs inside of it.  \n",
+    "We'll make a new CSV file called `animal_legs_tests.csv` and write our test inputs inside of it.  \n",
     "\n",
     "Promptfoo allows us to define evaluation logic directly inside the CSV file. In upcoming lessons we'll see some of the built-in test assertions that come with promptfoo, but for this particular evaluation all we need to do is look for an exact string match between the model's output and the expected output number of legs.\n",
     "\n",
@@ -209,7 +209,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Create a `dataset.csv` file and add the following to it: \n",
+    "Create a `animal_legs_tests.csv` file and add the following to it: \n",
     "\n",
     "```csv\n",
     "animal_statement,__expected\n",
@@ -232,7 +232,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, we'll tell promptfoo that it should use our `dataset.csv` file to load tests from.  To do this, update the `promptfooconfig.yaml` file to include this code: \n",
+    "Finally, we'll tell promptfoo that it should use our `animal_legs_tests.csv` file to load tests from.  To do this, update the `promptfooconfig.yaml` file to include this code: \n",
     "\n",
     "```yaml\n",
     "description: \"Animal Legs Eval\"\n",
diff --git a/prompt_evaluations/07_prompt_foo_custom_graders/lesson.ipynb b/prompt_evaluations/07_prompt_foo_custom_graders/lesson.ipynb
index b94a5f2..3cfa97b 100644
--- a/prompt_evaluations/07_prompt_foo_custom_graders/lesson.ipynb
+++ b/prompt_evaluations/07_prompt_foo_custom_graders/lesson.ipynb
@@ -205,7 +205,7 @@
     "\n",
     "We've opted to return a GradingResult dictionary, which must include the following properties:\n",
     "\n",
-    "- `pass_`: boolean\n",
+    "- `pass`: boolean\n",
     "-  `score`: float\n",
     "- `reason`: a string explanation\n",
     "\n",
diff --git a/prompt_evaluations/08_prompt_foo_model_graded/lesson.ipynb b/prompt_evaluations/08_prompt_foo_model_graded/lesson.ipynb
index 8bac09c..61ff19e 100644
--- a/prompt_evaluations/08_prompt_foo_model_graded/lesson.ipynb
+++ b/prompt_evaluations/08_prompt_foo_model_graded/lesson.ipynb
@@ -41,7 +41,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Mdel-graded evals with promptfoo\n",
+    "## Model-graded evals with promptfoo\n",
     "\n",
     "As with most things in promptfoo, there are multiple valid approaches to writing model-graded evaluations.  In this lesson we'll see the simplest pattern: utilizing built-in assertions.  In the next lesson, we'll see how to write our own custom model-graded assertion functions.\n",
     "\n",