DOC: Delete cookbooks directory and fix cross-references - PR #3

doc/blog/2025_02_11.md

@@ -32,6 +32,6 @@ See the updated documentation [here](../code/datasets/1_loading_datasets.ipynb).
 
 ## What else can we do with this?
 
-Now that we've loaded our dataset into PyRIT as a `SeedPromptDataset` the really exciting red teaming can begin. A great example of this is in our [Sending a Million Prompts](../cookbooks/1_sending_prompts.ipynb) notebook! We can use the prompts to evaluate the target by sending all the previously loaded prompts, modifying which attacks to use, and storing the scores for further analysis.
+Now that we've loaded our dataset into PyRIT as a `SeedPromptDataset` the really exciting red teaming can begin. A great example of this is in our [Baseline-Only Execution](../code/scenarios/9_baseline_only.ipynb) notebook! We can use the prompts to evaluate the target by sending all the previously loaded prompts, modifying which attacks to use, and storing the scores for further analysis.
 
 In this blog post, we've walked through how we use structured datasets through our `SeedPrompt` and `SeedPromptDataset` classes. PyRIT's architecture allows for customization at every stage - whether through converters or configuring different scorers - and seed prompts set us up to effectively probe for risks in AI systems. Send over any contributions to add more datasets, refine seed prompts, or any open issues on Github! Now that you understand a core component of PyRIT, go ahead and try it out!

doc/code/setup/1_configuration.ipynb

@@ -215,7 +215,7 @@
     "\n",
     "You can pass these in as arguments to every class initialization, but it can be a huge pain to set these every time. It would be nicer to just say out of the box that a scorer target LLM has a temperature of .5 by default, and a converter target LLM has a temperature of 1.1 by default. And it turns out you can!\n",
     "\n",
-    "The following example shows how to use PyRIT initializers. This tackles a similar scenario to the [Sending Prompts](../../cookbooks/1_sending_prompts.ipynb) but is much easier because defaults are set."
+    "The following example shows how to use PyRIT initializers. This tackles a similar scenario to [Baseline-Only Execution](../scenarios/9_baseline_only.ipynb) but is much easier because defaults are set."
    ]
   },
   {
@@ -398,7 +398,7 @@
     "    \"Explain the theory of relativity in simple terms\",\n",
     "]\n",
     "\n",
-    "# This is similar to the cookbook \"Sending a Million Prompts\" but using defaults\n",
+    "# This is similar to the \"Baseline-Only Execution\" scenario but using defaults\n",
     "\n",
     "# Create target without extensive configuration (uses defaults from initializer)\n",
     "endpoint = os.environ[\"OPENAI_CHAT_ENDPOINT\"]\n",

doc/code/setup/1_configuration.py

@@ -128,7 +128,7 @@
 #
 # You can pass these in as arguments to every class initialization, but it can be a huge pain to set these every time. It would be nicer to just say out of the box that a scorer target LLM has a temperature of .5 by default, and a converter target LLM has a temperature of 1.1 by default. And it turns out you can!
 #
-# The following example shows how to use PyRIT initializers. This tackles a similar scenario to the [Sending Prompts](../../cookbooks/1_sending_prompts.ipynb) but is much easier because defaults are set.
+# The following example shows how to use PyRIT initializers. This tackles a similar scenario to [Baseline-Only Execution](../scenarios/9_baseline_only.ipynb) but is much easier because defaults are set.
 
 # %%
 import os
@@ -172,7 +172,7 @@
     "Explain the theory of relativity in simple terms",
 ]
 
-# This is similar to the cookbook "Sending a Million Prompts" but using defaults
+# This is similar to the "Baseline-Only Execution" scenario but using defaults
 
 # Create target without extensive configuration (uses defaults from initializer)
 endpoint = os.environ["OPENAI_CHAT_ENDPOINT"]