diff --git a/qdrant-landing/content/documentation/tutorials/code-search.md b/qdrant-landing/content/documentation/tutorials/code-search.md
index cd0628fdb..284f9ef3b 100644
--- a/qdrant-landing/content/documentation/tutorials/code-search.md
+++ b/qdrant-landing/content/documentation/tutorials/code-search.md
@@ -20,8 +20,8 @@ source code itself, which is mostly written in Rust.
 We want to search codebases using natural semantic queries, and searching for
 code based on similar logic. You can set up these tasks with embeddings: 
 
-1. General usage neural encoder for natural-like queries, in our case `all-MiniLM-L6-v2`
-   from the
+1. General usage neural encoder for Natural Language Processing (NLP), in our case
+   `all-MiniLM-L6-v2` from the
    [sentence-transformers](https://www.sbert.net/docs/pretrained_models.html) library.
 2. Specialized embeddings for code-to-code similarity search. We use the
    `jina-embeddings-v2-base-code` model.
@@ -31,6 +31,11 @@ more closely resembles natural language. The Jina embeddings model supports a
 variety of standard programming languages, so there is no need to preprocess the
 snippets. We can use the code as is.
 
+NLP-based search is based on function signatures, but code search may return
+smaller pieces, such as loops. So, if we receive a particular function signature
+from the NLP model and part of its implementation from the code model, we merge
+the results and highlight the overlap.
+
 ## Data preparation
 
 Chunking the application sources into smaller parts is a non-trivial task. In
@@ -411,14 +416,30 @@ This is one example of how you can use different models and combine the results.
 In a real-world scenario, you might run some reranking and deduplication, as
 well as additional processing of the results.
 
-Our [Code search demo](https://github.com/qdrant/demo-code-search) uses
-both models. In the screenshot, we search for `flush of wal`. The result
+### Code search demo
+
+Our [Code search demo](https://code-search.qdrant.tech/) uses the following process:
+
+1. The user sends a query.
+1. Both models vectorize that query simultaneously. We get two different 
+   vectors.
+1. Both vectors are used in parallel to find relevant snippets. We expect 
+   5 examples from the NLP search and 20 examples from the code search.
+1. Once we retrieve results for both vectors, we merge them in one of the 
+   following scenarios: 
+   1. If both methods return different results, we prefer the results from 
+      the general usage model (NLP).
+   1. If there is an overlap between the search results, we merge overlapping
+      snippets.
+
+In the screenshot, we search for `flush of wal`. The result
 shows relevant code, merged from both models. Note the highlighted
 code in lines 621-629. It's where both models agree.
 
 ![Results from both models, with overlap](/documentation/tutorials/code-search/code-search-demo-example.png)
 
 Now you see semantic code intelligence, in action.
+
 ### Grouping the results
 
 You can improve the search results, by grouping them by payload properties.