diff --git a/.github/workflows/spellcheck.yaml b/.github/workflows/spellcheck.yaml
index fcc66ad40b..da7be3decc 100644
--- a/.github/workflows/spellcheck.yaml
+++ b/.github/workflows/spellcheck.yaml
@@ -17,6 +17,6 @@ jobs:
uses: actions/checkout@v6
- name: Spell Check Repo
- uses: crate-ci/typos@v1.43.5
+ uses: crate-ci/typos@v1.44.0
with:
files: docs/**/**/*.md docs/**/**/*.mdx
diff --git a/docs/genai/04_how_to_guides/02_embeddings.mdx b/docs/genai/04_how_to_guides/02_embeddings.mdx
index 972313dab2..a43dca4df0 100644
--- a/docs/genai/04_how_to_guides/02_embeddings.mdx
+++ b/docs/genai/04_how_to_guides/02_embeddings.mdx
@@ -4,7 +4,7 @@ While Decoder-only LLMs gained massive popularity via their usage in chatbots, E
```mermaid
flowchart LR;
- A["natual language text:
*GenAI can be used for research*"]
+ A["natural language text:
*GenAI can be used for research*"]
B["encoder-only LLM"]
C["vector embedding
[0.052, 0.094, 0.244, ...]"]
A-- "Input" -->B;
@@ -12,7 +12,7 @@ flowchart LR;
```
:::tip
-Embeddings have the ability to encode the semantic meaning of the natual language text/images!
+Embeddings have the ability to encode the semantic meaning of the natural language text/images!
:::
The snippet below uses the `text-embedding-3-small` model to create 32-dimensional floating point vector embeddings for the input string:
diff --git a/docs/genai/04_how_to_guides/03_retrieval_augmented_generation.mdx b/docs/genai/04_how_to_guides/03_retrieval_augmented_generation.mdx
index 9c82e00e85..a92a6fc1e7 100644
--- a/docs/genai/04_how_to_guides/03_retrieval_augmented_generation.mdx
+++ b/docs/genai/04_how_to_guides/03_retrieval_augmented_generation.mdx
@@ -17,7 +17,7 @@ flowchart TB;
C["encoder-only LLM"]
D@{shape: procs, label: "text chunk embedding"}
E[("vector database")]
- F["natual language prompt"]
+ F["natural language prompt"]
G["query embedding"]
I["relevant chunks"]
J["original prompt with added context"]