feat(docs): add some (not shown) content

docs/components/dataset.md

@@ -103,9 +103,10 @@ cse[0]
 
 ## Loading a List
 
-Of course, this is not the only way to load data. If you have any other inputs, one of the easiest ways to load it into our system is to simply read it into a **list of dictionaries**. Here is an example:
+Of course, this is not the only way to load data. If you have any other inputs, one of the easiest ways to load it into our system is to simply read it into a **list of dictionaries** and **list of strings**. Here is an example:
 
 ```python linenums="45"
+# load list of dictionaries
 raw_data = [
     {"prompt": "what's up"},
     {"prompt": "hey!"},
@@ -117,6 +118,18 @@ dummy_dataset = HuggingFaceDataset[Prompt].from_list("dummy", raw_data)
 dummy_dataset.name
 # "dummy"
 
+dummy_dataset[0]
+# Prompt(prompt="what's up")
+
+# load list of strings
+raw_data = [
+	"what's up",
+	"hey!",
+	"why is the world so curle"
+]
+
+dummy_dataset = HuggingFaceDataset[Prompt].from_list("dummy", raw_data)
+
 dummy_dataset[0]
 # Prompt(prompt="what's up")
 ```

docs/components/judges.md

@@ -0,0 +1 @@
+# Judges

docs/components/templates.md

@@ -0,0 +1,6 @@
+# Prompt Templates
+
+WalledEval strives to provide a method to build easy-to-use templates for researchers and testers alike to use for all kinds of different tasks. Whether it be a template to [automatically mutate prompts](auto-red-teaming.md) or to [prompt LLMs to perform like judges](quickstart/judges.md), prompt templates take up a major portion of 
+
+
+Beyond just loading data, our libray provides methods to load adversarial Prompt Templates like [DAN](https://github.com/verazuo/jailbreak_llms) and [DeepInception](https://github.com/tmlr-group/DeepInception). The team of WalledEval has compiled an extensive dataset of Prompt Templates from several papers, datasets and codebases, with more to come. We hope to use this to standardise a potential practice of keeping strings out of the codebase.

docs/quickstart/templates.md

@@ -4,7 +4,7 @@ Beyond general
 
 With the advent of LLMs being used for mutation, inference and judges, prompt templates have become a recurring feature in various parts of the general evaluation framework. Sadly, since [`promptsource`](https://github.com/bigscience-workshop/promptsource), there hasn't been much effort made in compiling a large variety of prompt templates in one centralised system. This is where our **prompt database** comes in! WalledEval compiles prompts from many, many papers in prior literature to consolidate a huge database to choose from.
 
-WalledEval strives to provide a method to build easy-to-use templates for researchers and testers alike to use for all kinds of different tasks. Whether it be a template to [automatically mutate prompts](auto-red-teaming.md) or to [prompt LLMs to perform like judges](judges.md), prompt templates take up a major portion of 
+WalledEval strives to provide a method to build easy-to-use templates for researchers and testers alike to use for all kinds of different tasks. Whether it be a template to [automatically mutate prompts](auto-red-teaming.md) or to [prompt LLMs to perform like judges](quickstart/judges.md), prompt templates take up a major portion of 
 
 
 Beyond just loading data, our libray provides methods to load adversarial Prompt Templates like [DAN](https://github.com/verazuo/jailbreak_llms) and [DeepInception](https://github.com/tmlr-group/DeepInception). The team of WalledEval has compiled an extensive dataset of Prompt Templates from several papers, datasets and codebases, with more to come. We hope to use this to standardise a potential practice of keeping strings out of the codebase.