Update README.md

README.md

@@ -11,8 +11,15 @@ pinned: false
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+## Exploratory Data Analysis (EDA) & Dataset Details
 
-## 3. Embedding Model Selection
+Please note that the comprehensive Exploratory Data Analysis (EDA)—including data quality checks, distribution analysis, and the full dataset card—is hosted in a separate dataset repository to keep this application space clean and focused. 
+
+You can explore the complete EDA and access the raw dataset in the README at:
+🔗 [Davichick/InterviewForge_GenDS](https://huggingface.co/datasets/Davichick/InterviewForge_GenDS)
+
+
+## 1. Embedding Model Selection
 
 To power the retrieval engine, I evaluated three open-source, 384-dimensional sentence-embedding models from Hugging Face: `all-MiniLM-L6-v2`, `BAAI/bge-small-en-v1.5`, and `intfloat/e5-small-v2`. The evaluation ran on a random sample of 2,500 questions and measured three things:
 
@@ -36,7 +43,7 @@ One implementation detail worth noting: `e5-small-v2` is an asymmetric retrieval
 
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-## 4. Embeddings Analysis & Clustering
+## 2. Embeddings Analysis & Clustering
 
 To validate that the chosen model organizes the interview domain in a meaningful way, the embedding space was analyzed with three complementary techniques: K-Means clustering with the Elbow Method, t-SNE projection, and hierarchical clustering.
 
@@ -71,7 +78,7 @@ The fact that this taxonomy matches how humans intuitively divide interviews —
 
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-## 5. Recommendation System (How it Works)
+## 3. Recommendation System (How it Works)
 The application utilizes a Vector-Based Retrieval strategy to recommend the most relevant interview question to the user based on their specific configuration.
 
 **The Pipeline:**
@@ -80,3 +87,16 @@ The application utilizes a Vector-Based Retrieval strategy to recommend the most
 3. **Query Embedding:** The input query is passed through the selected `e5-small-v2` model, converting it into a 384-dimensional query vector.
 4. **Vector Similarity Search:** The system computes the **Cosine Similarity** between the user's query vector and the pre-computed question embeddings database.
 5. **Retrieval:** The system identifies the vector with the highest similarity score (argmax) and retrieves the exact matching interview scenario.
+
+
+## 4. Dual-Model Architecture: Retrieval vs. Evaluation
+
+
+
+* **Model 1: The Retrieval Engine (`intfloat/e5-small-v2`)**
+  When the interview begins, this embedding model handles the search. It translates the user's specific configuration into a 384-dimensional vector and searches the 20,444-question database to fetch the exact, context-aware question. It does not generate new text; it retrieves the best existing scenario.
+
+* **Model 2: The Evaluation Engine (`Qwen/Qwen2.5-1.5B-Instruct`)**
+  Once the user submits their answer to the question, a completely different Generative AI (LLM) takes over. This model acts as the "Interviewer." It analyzes the candidate's specific response against the original question and generates a personalized, structured evaluation. The output includes a numeric Grade, highlighted Pros and Cons, and a concrete Example of how to answer it better.
+
+**Why this matters:** This separation of concerns ensures the application is highly efficient. It uses aמ embedding model for the heavy lifting of searching through thousands of records, while reserving the deeper, more computationally expensive Generative LLM exclusively for providing human-like, nuanced feedback.