diff --git a/README.md b/README.md
index 6879e734..dfb53708 100644
--- a/README.md
+++ b/README.md
@@ -42,8 +42,8 @@ You can interact with the agents as you would with any LLM or LMM model:
 ```python
 >>> import vision_agent as va
 >>> agent = VisionAgent()
->>> agent("How many apples are in this image?", image="apples.jpg")
-"There are 2 apples in the image."
+>>> agent("What percentage of the area of this jar is filled with coffee beans?", image="jar.jpg")
+"The percentage of area of the jar filled with coffee beans is 25%."
 ```
 
 To better understand how the model came up with it's answer, you can also run it in
@@ -57,22 +57,22 @@ You can also have it return the workflow it used to complete the task along with
 the individual steps and tools to get the answer:
 
 ```python
->>> resp, workflow = agent.chat_with_workflow([{"role": "user", "content": "How many apples are in this image?"}], image="apples.jpg")
+>>> resp, workflow = agent.chat_with_workflow([{"role": "user", "content": "What percentage of the area of this jar is filled with coffee beans?"}], image="jar.jpg")
 >>> print(workflow)
-[{"task": "Count the number of apples using 'grounding_dino_'.",
-  "tool": "grounding_dino_",
-  "parameters": {"prompt": "apple", "image": "apples.jpg"},
+[{"task": "Segment the jar using 'grounding_sam_'.",
+  "tool": "grounding_sam_",
+  "parameters": {"prompt": "jar", "image": "jar.jpg"},
   "call_results": [[
     {
-      "labels": ["apple", "apple"],
-      "scores": [0.99, 0.95],
+      "labels": ["jar"],
+      "scores": [0.99],
       "bboxes": [
         [0.58, 0.2, 0.72, 0.45],
-        [0.94, 0.57, 0.98, 0.66],
-      ]
+      ],
+      "masks": "mask.png"
     }
   ]],
-  "answer": "There are 2 apples in the image.",
+  "answer": "The jar is located at [0.58, 0.2, 0.72, 0.45].",
 }]
 ```
 
@@ -84,13 +84,12 @@ you. For example:
 ```python
 >>> import vision_agent as va
 >>> llm = va.llm.OpenAILLM()
->>> detector = llm.generate_detector("Can you build an apple detector for me?")
->>> detector("apples.jpg")
-[{"labels": ["apple", "apple"],
-  "scores": [0.99, 0.95],
+>>> detector = llm.generate_detector("Can you build a jar detector for me?")
+>>> detector("jar.jpg")
+[{"labels": ["jar",],
+  "scores": [0.99],
   "bboxes": [
     [0.58, 0.2, 0.72, 0.45],
-    [0.94, 0.57, 0.98, 0.66],
   ]
 }]
 ```
diff --git a/docs/old.md b/docs/lmms_and_datastore.md
similarity index 87%
rename from docs/old.md
rename to docs/lmms_and_datastore.md
index c585fd03..302b9732 100644
--- a/docs/old.md
+++ b/docs/lmms_and_datastore.md
@@ -1,21 +1,3 @@
-<p align="center">
-  <img width="100" height="100" src="https://github.com/landing-ai/landingai-python/raw/main/assets/avi-logo.png">
-</p>
-
-# Welcome to the Landing AI LMM Tools Documentation
-
-This library provides a set of tools to help you build applications with Large Multimodal Model (LMM).
-
-
-## Quick Start
-
-### Install
-First, install the library:
-
-```bash
-pip install vision-agent
-```
-
 ### LMMs
 One of the problems of dealing with image data is it can be difficult to organize and
 search. For example, you might have a bunch of pictures of houses and want to count how