diff --git a/README.md b/README.md
index 993fdf1..4ea5115 100644
--- a/README.md
+++ b/README.md
@@ -53,14 +53,18 @@ BlendSQL allows us to ask the following questions by injecting "ingredients", wh
_Which parks don't have park facilities?_
```sql
-SELECT * FROM parks
+SELECT "Name", "Description" FROM parks
WHERE NOT {{
LLMValidate(
'Does this location have park facilities?',
- context=(SELECT "Name" AS "Park", "Description" FROM parks),
+ context=(SELECT "Name" AS "Park", "Description" FROM parks)
)
}}
```
+| Name | Description |
+|:----------------|:---------------------------------------------------------------------------------------------------------------------------------------|
+| Everglades | The country's northernmost park protects an expanse of pure wilderness in Alaska's Brooks Range and has no park facilities. |
+
_What does the largest park in Alaska look like?_
@@ -77,17 +81,29 @@ WHERE "Location" = 'Alaska'
ORDER BY "Size in km" DESC LIMIT 1
```
+| Name | Image Description | Size in km |
+|:-----------|:--------------------------------------------------------|-------------:|
+| Everglades | A forest of tall trees with a sunset in the background. | 30448.1 |
+
+
+
_Which state is the park in that protects an ash flow?_
```sql
-SELECT "Location" FROM parks WHERE "Name" = {{
- LLMQA(
- 'Which park protects an ash flow?',
- context=(SELECT "Name", "Description" FROM parks),
- options="parks::Name"
- )
-}}
+SELECT "Location", "Name" AS "Park Protecting Ash Flow" FROM parks
+ WHERE "Name" = {{
+ LLMQA(
+ 'Which park protects an ash flow?',
+ context=(SELECT "Name", "Description" FROM parks),
+ options="parks::Name"
+ )
+ }}
```
+| Location | Park Protecting Ash Flow |
+|:-----------|:---------------------------|
+| Alaska | Katmai |
+
+
_How many parks are located in more than 1 state?_
@@ -95,6 +111,10 @@ _How many parks are located in more than 1 state?_
SELECT COUNT(*) FROM parks
WHERE {{LLMMap('How many states?', 'parks::Location')}} > 1
```
+| Count |
+|--------:|
+| 1 |
+
Now, we have an intermediate representation for our LLM to use that is explainable, debuggable, and [very effective at hybrid question-answering tasks](https://arxiv.org/abs/2402.17882).