diff --git a/apps/docs/docs/advanced.md b/apps/docs/docs/advanced.md
index dd35a00cbde91a3c12f6a503202e0d20f9f010de..805d0362410937db47154169bc9c162e1dfde8b9 100644
--- a/apps/docs/docs/advanced.md
+++ b/apps/docs/docs/advanced.md
@@ -1,5 +1,5 @@
 ---
-sidebar_position: 4
+sidebar_position: 5
 ---
 
 # Advanced Features
@@ -21,3 +21,7 @@ npx ts-node subquestion.ts
 SubQuestionQueryEngine is implemented with Tools. The basic idea of Tools is that they are executable options for the large language model. In this case, our SubQuestionQueryEngine relies on QueryEngineTool, which as you guessed it is a tool to run queries on a QueryEngine. This allows us to give the model an option to query different documents for different questions for example. You could also imagine that the SubQuestionQueryEngine could use a Tool that searches for something on the web or gets an answer using Wolfram Alpha.
 
 You can learn more about Tools by taking a look at the LlamaIndex Python documentation https://gpt-index.readthedocs.io/en/latest/core_modules/agent_modules/tools/root.html
+
+## ListIndexLLMRetriever
+
+Todo: Insert text.
diff --git a/apps/docs/docs/api/_category_.yml b/apps/docs/docs/api/_category_.yml
index 01a5012291c203b622c44e6ed330e4b075c3291a..79eb4b8a2939888be2393145205ffe6bc5fea186 100644
--- a/apps/docs/docs/api/_category_.yml
+++ b/apps/docs/docs/api/_category_.yml
@@ -1,2 +1,2 @@
 label: "API"
-position: 4
\ No newline at end of file
+position: 6
\ No newline at end of file
diff --git a/apps/docs/docs/end_to_end.md b/apps/docs/docs/end_to_end.md
index eb47c4727492f03693cbbe3ef472207a3f4bf3db..e4cc66696b93fc6c7969f6ae8a6f094ec08d218b 100644
--- a/apps/docs/docs/end_to_end.md
+++ b/apps/docs/docs/end_to_end.md
@@ -1,3 +1,7 @@
+---
+sidebar_position: 3
+---
+
 # End to End Examples
 
 We include several end-to-end examples using LlamaIndex.TS in the repository
diff --git a/apps/docs/docs/modules/_category_.yml b/apps/docs/docs/modules/_category_.yml
index 83298fde65a29f19ffbd0353e90ee86aed6539d0..7af5ad40f477c335c5070dfbe0b6266ae4be7598 100644
--- a/apps/docs/docs/modules/_category_.yml
+++ b/apps/docs/docs/modules/_category_.yml
@@ -1,3 +1,3 @@
 label: "Modules"
 collapsed: false
-position: 3
\ No newline at end of file
+position: 4
\ No newline at end of file
diff --git a/apps/docs/docs/modules/high_level/chat_engine.md b/apps/docs/docs/modules/high_level/chat_engine.md
index c928feddbfc37afacecd473bfbbdd6c9947049bb..5b00b89d7f0ea5d27ee3bf45daa6e2d38910b7f3 100644
--- a/apps/docs/docs/modules/high_level/chat_engine.md
+++ b/apps/docs/docs/modules/high_level/chat_engine.md
@@ -1,5 +1,15 @@
 ---
-sidebar_position: 3
+sidebar_position: 4
 ---
 
-# ChatEngine
\ No newline at end of file
+# ChatEngine
+
+The chat engine is a quick and simple way to chat with the data in your index.
+
+```typescript
+const retriever = index.asRetriever();
+const chatEngine = new ContextChatEngine({ retriever });
+
+// start chatting
+const response = await chatEngine.chat(query);
+```
diff --git a/apps/docs/docs/modules/high_level/data_index.md b/apps/docs/docs/modules/high_level/data_index.md
index c28e771e1e716d91c4a9dfb886375368aca229d3..3645d34df32355acdca655602ec8f206b1fbc22e 100644
--- a/apps/docs/docs/modules/high_level/data_index.md
+++ b/apps/docs/docs/modules/high_level/data_index.md
@@ -1,5 +1,23 @@
 ---
-sidebar_position: 1
+sidebar_position: 2
 ---
 
-# Index
\ No newline at end of file
+# Index
+
+An index is the basic container and organization for your data. LlamaIndex.TS supports two indexes:
+
+- `ListIndex` - will send every `Node` in the index to the LLM in order to generate a response
+- `VectorStoreIndex` - will send the top-k `Node`s to the LLM when generating a response. The default top-k is 2.
+
+```typescript
+import {
+  Document,
+  VectorStoreIndex,
+} from "llamaindex";
+
+const document = new Document({ text: "test" });
+
+const index = await VectorStoreIndex.fromDocuments(
+  [document]
+);
+```
\ No newline at end of file
diff --git a/apps/docs/docs/modules/high_level/data_loader.md b/apps/docs/docs/modules/high_level/data_loader.md
new file mode 100644
index 0000000000000000000000000000000000000000..8943d37e2ec7f69d4b44d762b48eddce52775750
--- /dev/null
+++ b/apps/docs/docs/modules/high_level/data_loader.md
@@ -0,0 +1,13 @@
+---
+sidebar_position: 1
+---
+
+# Reader / Loader
+
+LlamaIndex.TS supports easy loading of files from folders using the `SimpleDirectoryReader` class. Currently, `.txt` and `.pdf` files are supported, with more planned in the future!
+
+```typescript
+import { SimpleDirectoryReader } from "llamaindex";
+
+documents = new SimpleDirectoryReader().loadData("./data");
+```
diff --git a/apps/docs/docs/modules/high_level/documents_and_nodes.md b/apps/docs/docs/modules/high_level/documents_and_nodes.md
index 20489b84256e31c67742ec9b7e7295c786270d49..08ba6b73aba5982125fea0029253d34b82a26a9a 100644
--- a/apps/docs/docs/modules/high_level/documents_and_nodes.md
+++ b/apps/docs/docs/modules/high_level/documents_and_nodes.md
@@ -6,8 +6,8 @@ sidebar_position: 0
 
 `Document`s and `Node`s are the basic building blocks of any index. While the API for these objects is similar, `Document` objects represent entire files, while `Node`s are smaller pieces of that original document, that are suitable for an LLM and Q&A.
 
-## Usage Pattern
-
 ```typescript
+import { Document } from "llamaindex";
 
-```
\ No newline at end of file
+document = new Document({ text: "text", metadata: { "key": "val" }});
+```
diff --git a/apps/docs/docs/modules/high_level/query_engine.md b/apps/docs/docs/modules/high_level/query_engine.md
index f44cdc40b131b181445b1af93a0ed34c06fcaf84..88a926c76a8cec973cd81657d3a31f8008e2edba 100644
--- a/apps/docs/docs/modules/high_level/query_engine.md
+++ b/apps/docs/docs/modules/high_level/query_engine.md
@@ -1,5 +1,12 @@
 ---
-sidebar_position: 2
+sidebar_position: 3
 ---
 
-# QueryEngine
\ No newline at end of file
+# QueryEngine
+
+A query engine wraps a `Retriever` and a `ResponseSynthesizer` into a pipeline, that will use the query string to fetech nodes and then send them to the LLM to generate a response.
+
+```typescript
+const queryEngine = index.asQueryEngine();
+const response = queryEngine.query("query string");
+```
\ No newline at end of file
diff --git a/apps/docs/docs/modules/index.md b/apps/docs/docs/modules/index.md
index 3213983d50abc4032ef2ec93d525f0a6cd7a8c19..d51ba9c05ca2fb28c11455ec53b6c238500ce412 100644
--- a/apps/docs/docs/modules/index.md
+++ b/apps/docs/docs/modules/index.md
@@ -8,6 +8,8 @@ LlamaIndex.TS offers several core modules, seperated into high-level modules for
 
 - **Node**: The basic data building block. Most commonly, these are parts of the document split into manageable pieces that are small enough to be fed into an embedding model and LLM.
 
+- **Reader/Loader**: A reader or loader is something that takes in a document in the real world and transforms into a Document class that can then be used in your Index and queries. We currently support plain text files and PDFs with many many more to come.
+
 - **Indexes**: indexes store the Nodes and the embeddings of those nodes.
 
 - **QueryEngine**: Query engines are what generate the query you put in and give you back the result. Query engines generally combine a pre-built prompt with selected nodes from your Index to give the LLM the context it needs to answer your query.
@@ -22,8 +24,6 @@ LlamaIndex.TS offers several core modules, seperated into high-level modules for
 
 - **Embedding**: An embedding is represented as a vector of floating point numbers. OpenAI's text-embedding-ada-002 is our default embedding model and each embedding it generates consists of 1,536 floating point numbers. Another popular embedding model is BERT which uses 768 floating point numbers to represent each Node. We provide a number of utilities to work with embeddings including 3 similarity calculation options and Maximum Marginal Relevance
 
-- **Reader/Loader**: A reader or loader is something that takes in a document in the real world and transforms into a Document class that can then be used in your Index and queries. We currently support plain text files and PDFs with many many more to come.
-
 - **TextSplitter**: Text splitting strategies are incredibly important to the overall efficacy of the embedding search. Currently, while we do have a default, there's no one size fits all solution. Depending on the source documents, you may want to use different splitting sizes and strategies. Currently we support spliltting by fixed size, splitting by fixed size with overlapping sections, splitting by sentence, and splitting by paragraph.
 
 - **Retriever**: The Retriever is what actually chooses the Nodes to retrieve from the index. Here, you may wish to try retrieving more or fewer Nodes per query, changing your similarity function, or creating your own retriever for each individual use case in your application. For example, you may wish to have a separate retriever for code content vs. text content.
diff --git a/apps/docs/docs/modules/low_level/data_loader.md b/apps/docs/docs/modules/low_level/data_loader.md
deleted file mode 100644
index d603f12d910d3320758afe51ae2c858eef941319..0000000000000000000000000000000000000000
--- a/apps/docs/docs/modules/low_level/data_loader.md
+++ /dev/null
@@ -1,5 +0,0 @@
----
-sidebar_position: 2
----
-
-# Reader / Loader
\ No newline at end of file
diff --git a/apps/docs/docs/modules/low_level/embedding.md b/apps/docs/docs/modules/low_level/embedding.md
index e3178026174e57faa1939115bc9dfff3fd02e7da..edf16a925f2033483c3ab20f72802b28869eed41 100644
--- a/apps/docs/docs/modules/low_level/embedding.md
+++ b/apps/docs/docs/modules/low_level/embedding.md
@@ -2,4 +2,16 @@
 sidebar_position: 1
 ---
 
-# Embedding
\ No newline at end of file
+# Embedding
+
+The embedding model in LlamaIndex is responsible for creating numerical representations of text. By default, LlamaIndex will use the `text-embedding-ada-002` model from OpenAI. 
+
+This can be explicitly set in the `ServiceContext` object.
+
+```typescript
+import { OpenAIEmbedding, ServiceContext } from "llamaindex";
+
+const openaiEmbeds = new OpenAIEmbedding();
+
+const serviceContext = new ServiceContext({ embedModel: openaiEmbeds });
+```
\ No newline at end of file
diff --git a/apps/docs/docs/modules/low_level/llm.md b/apps/docs/docs/modules/low_level/llm.md
index dc14840bc0c40c09abbfbc714b8524f4900eb04b..ce943a85f0ea5f0d452742955e956e6fb9348599 100644
--- a/apps/docs/docs/modules/low_level/llm.md
+++ b/apps/docs/docs/modules/low_level/llm.md
@@ -2,4 +2,16 @@
 sidebar_position: 0
 ---
 
-# LLM
\ No newline at end of file
+# LLM
+
+The LLM is responsible for reading text and generating natural language responses to queries. By default, LlamaIndex.TS uses `gpt-3.5-turbo`. 
+
+The LLM can be explicitly set in the `ServiceContext` object.
+
+```typescript
+import { ChatGPTLLMPredictor, ServiceContext } from "llamaindex";
+
+const openaiLLM = new ChatGPTLLMPredictor({ model: "gpt-3.5-turbo" });
+
+const serviceContext = new ServiceContext({ llmPredictor: openaiLLM });
+```
diff --git a/apps/docs/docs/modules/low_level/retriever.md b/apps/docs/docs/modules/low_level/retriever.md
index 52c81fd51a617de08977b0b77cedf100c5bcb06b..2976ae2e0fc9978492f8c1cdc397ec42f6e8d68f 100644
--- a/apps/docs/docs/modules/low_level/retriever.md
+++ b/apps/docs/docs/modules/low_level/retriever.md
@@ -2,4 +2,14 @@
 sidebar_position: 5
 ---
 
-# Retriever
\ No newline at end of file
+# Retriever
+
+A retriever in LlamaIndex is what is used to fetch `Node`s from an index using a query string. For example, a `ListIndexRetriever` will fetch all nodes no matter the query. Meanwhile, a `VectorIndexRetriever` will only fetch the top-k most similar nodes.
+
+```typescript
+const retriever = vector_index.asRetriever()
+retriever.similarityTopK = 3;
+
+// Fetch nodes!
+const nodes = await retriever.aretrieve("query string");
+```
diff --git a/apps/docs/docs/modules/low_level/simple_prompt.md b/apps/docs/docs/modules/low_level/simple_prompt.md
index f1960bcd3c380e523ac385c9a06007ebf222abea..e10aed1041adb85291bd6eb3c48ad111e72695d6 100644
--- a/apps/docs/docs/modules/low_level/simple_prompt.md
+++ b/apps/docs/docs/modules/low_level/simple_prompt.md
@@ -3,3 +3,5 @@ sidebar_position: 4
 ---
 
 # SimplePrompt
+
+Prompts are the basic inputs to an LLM. These can contain variables that get filled in later, or just plain text that is sent as-is to a model.
diff --git a/apps/docs/docs/modules/low_level/storage.md b/apps/docs/docs/modules/low_level/storage.md
index c6c26edfc5e9c2afa1a96a78acf32d7cb8cb7011..b7423946e83b06d5e85d0b07e00e51e0d38e3518 100644
--- a/apps/docs/docs/modules/low_level/storage.md
+++ b/apps/docs/docs/modules/low_level/storage.md
@@ -2,4 +2,6 @@
 sidebar_position: 6
 ---
 
-# Storage
\ No newline at end of file
+# Storage
+
+Todo: Find out how this works.
\ No newline at end of file
diff --git a/apps/docs/docs/modules/low_level/text_splitter.md b/apps/docs/docs/modules/low_level/text_splitter.md
index 1c97febe0bd83411bc6e59b327c02556559e3338..0375e03ffd9081c2016d54a1864e3d9cbb53788f 100644
--- a/apps/docs/docs/modules/low_level/text_splitter.md
+++ b/apps/docs/docs/modules/low_level/text_splitter.md
@@ -2,4 +2,6 @@
 sidebar_position: 3
 ---
 
-# TextSplitter
\ No newline at end of file
+# TextSplitter
+
+Todo: Talk about text splitter. Alternatively, replace this with node parser? Or combine them?