diff --git a/docs/benchmarks.md b/docs/benchmarks.md
index 977e1927..e1fc2bf7 100644
--- a/docs/benchmarks.md
+++ b/docs/benchmarks.md
@@ -1,13 +1,27 @@
-# `haiku.rag` benchmarks
+# Benchmarks
 
-We use [repliqa](https://huggingface.co/datasets/ServiceNow/repliqa) for the evaluation of `haiku.rag`
+We use the [repliqa](https://huggingface.co/datasets/ServiceNow/repliqa) dataset for the evaluation of `haiku.rag`.
 
-* Recall
+You can perform your own evaluations using as example the script found at
+`tests/generate_benchmark_db.py`.
 
-We load the `News Stories` from `repliqa_3` which is 1035 documents, using `tests/generate_benchmark_db.py`, using the `mxbai-embed-large` Ollama embeddings.
+## Recall
 
-Subsequently, we run a search over the `question` for each row of the dataset and check whether we match the document that answers the question. The recall obtained is ~0.75 for matching in the top result, raising to ~0.75 for the top 3 results.
+In order to calculate recall, we load the `News Stories` from `repliqa_3` which is 1035 documents and index them in a sqlite db. Subsequently, we run a search over the `question` field for each row of the dataset and check whether we match the document that answers the question.
 
-* Question/Answer evaluation
 
-We use the  `News Stories` from `repliqa_3` using the `mxbai-embed-large` Ollama embeddings, with a QA agent also using Ollama with the `qwen3` model (8b). For each story we ask the `question` and use an LLM judge (also `qwen3`) to evaluate whether the answer is correct or not. Thus we obtain accuracy of ~0.54.
+The recall obtained is ~0.73 for matching in the top result, raising to ~0.75 for the top 3 results.
+
+| Model                                 | Document in top 1 | Document in top 3 |
+|---------------------------------------|-------------------|-------------------|
+| Ollama / `mxbai-embed-large`          | 0.73              | 0.75              |
+| OpenAI / `text-embeddings-3-small`    |                   |                   |
+
+## Question/Answer evaluation
+
+Again using the same dataset, we use a QA agent to answer the question. In addition we use an LLM judge (using the Ollama `qwen3`) to evaluate whether the answer is correct or not. The obtained accuracy is as follows:
+
+| Embedding Model              | QA Model                          | Accuracy  |
+|------------------------------|-----------------------------------|-----------|
+| Ollama / `mxbai-embed-large` | Ollama / `qwen3`                  | 0.64      |
+| Ollama / `mxbai-embed-large` | Anthropic / `Claude Sonnet 3.7`   | 0.79      |
diff --git a/mkdocs.yml b/mkdocs.yml
index e9767caa..07bcb523 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -63,6 +63,7 @@ nav:
       - Server: server.md
       - MCP: mcp.md
       - Python: python.md
+      - Benchmarks: benchmarks.md
 markdown_extensions:
   - admonition
   - attr_list
diff --git a/tests/generate_benchmark_db.py b/tests/generate_benchmark_db.py
index b1ade523..20bb01b2 100644
--- a/tests/generate_benchmark_db.py
+++ b/tests/generate_benchmark_db.py
@@ -106,6 +106,7 @@ async def run_qa_benchmark(k: int | None = None):
             if is_equivalent:
                 correct_answers += 1
             total_questions += 1
+            print("Current score:", correct_answers, "/", total_questions)
 
     accuracy = correct_answers / total_questions if total_questions > 0 else 0