Multimodal RAG in Prompt Engineering / GenAI - Model Metrics & Evaluation

Metrics & Evaluation - Multimodal RAG

Which metric matters for Multimodal RAG and WHY

Multimodal RAG (Retrieval-Augmented Generation) combines text and images or other data types to answer questions or generate content. The key metrics are Recall and F1 score. Recall is important because the model must find the right information from many sources. F1 balances recall with precision, showing how accurate and complete the answers are. For generation quality, BLEU or ROUGE scores help measure how close the output is to good answers.

Confusion Matrix for Multimodal RAG (Example)

      | Predicted Relevant | Predicted Irrelevant |
      |--------------------|----------------------|
      | True Positive (TP): 80  | False Negative (FN): 20 |
      | False Positive (FP): 15 | True Negative (TN): 85 |

      Total samples = 80 + 20 + 15 + 85 = 200

      Precision = TP / (TP + FP) = 80 / (80 + 15) = 0.842
      Recall = TP / (TP + FN) = 80 / (80 + 20) = 0.8
      F1 Score = 2 * (Precision * Recall) / (Precision + Recall) ≈ 0.82
    

Precision vs Recall Tradeoff with Examples

In Multimodal RAG, high recall means the model finds most relevant info, which is good for thorough answers. But this can lower precision, causing some wrong info to appear.

Example 1: A medical assistant using RAG must have high recall to not miss any important symptoms, even if some extra info is included.

Example 2: A customer support bot should have high precision to avoid giving wrong answers, even if it misses some less common questions.

What Good vs Bad Metric Values Look Like

Good: Precision and recall both above 0.8, F1 score near 0.8 or higher, BLEU/ROUGE scores showing close match to expected answers.

Bad: Precision or recall below 0.5, meaning many wrong or missed answers. Low F1 score below 0.6 shows imbalance. BLEU/ROUGE scores near zero mean poor generation quality.

Common Metrics Pitfalls

• Accuracy paradox: High accuracy can be misleading if data is imbalanced (e.g., many irrelevant items).
• Data leakage: If retrieval uses future info, metrics look better but model fails in real use.
• Overfitting: Very high training scores but low test scores mean model memorizes data, not generalizes.
• Ignoring multimodal balance: Metrics only on text or images separately miss how well the model combines both.

Self-Check Question

Your Multimodal RAG model has 98% accuracy but only 12% recall on relevant info. Is it good for production? Why or why not?

Answer: No, it is not good. The very low recall means the model misses most relevant information, which is critical for retrieval tasks. High accuracy here is misleading because most data is irrelevant, so the model guesses irrelevant too often. You need to improve recall to ensure the model finds the right info.

Key Result

Recall and F1 score are key to measure how well Multimodal RAG finds and balances relevant information across data types.