Merge branch 'main' into feature/litellm

Giskard-AI · Nov 21, 2024 · 5f39da1 · 5f39da1
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diff --git a/docs/open_source/testset_generation/rag_evaluation/index.md b/docs/open_source/testset_generation/rag_evaluation/index.md
@@ -4,7 +4,7 @@ After automatically generating a test set for your RAG agent using RAGET, you ca
 of the agent's answers** compared to the reference answers (using a LLM-as-a-judge approach). The main purpose
 of this evaluation is to help you **identify the weakest components in your RAG agent**.
 
-> ℹ️ You can find a [tutorial](../../../reference/notebooks/RAGET.ipynb) where we demonstrate the capabilities of RAGET
+> ℹ️ You can find a [tutorial](../../../reference/notebooks/RAGET_IPCC.ipynb) where we demonstrate the capabilities of RAGET
 > with a simple RAG agent build with LlamaIndex
 > on the IPCC report.
 

diff --git a/docs/open_source/testset_generation/testset_generation/index.md b/docs/open_source/testset_generation/testset_generation/index.md
@@ -6,7 +6,7 @@ an in-house evaluation dataset is a painful task that requires manual curation a
 To help with this, the Giskard python library provides **RAGET: RAG Evaluation Toolkit**, a toolkit to evaluate RAG
 agents **automatically**.
 
-> ℹ️ You can find a [tutorial](../../../reference/notebooks/RAGET.ipynb) where we demonstrate the capabilities of RAGET
+> ℹ️ You can find a [tutorial](../../../reference/notebooks/RAGET_IPCC.ipynb) where we demonstrate the capabilities of RAGET
 > with a simple RAG agent build with LlamaIndex
 > on the IPCC report.