Hybrid search strategies in Prompt Engineering / GenAI - Model Metrics & Evaluation

Metrics & Evaluation - Hybrid search strategies

Which metric matters for Hybrid Search Strategies and WHY

Hybrid search combines two ways to find answers: exact matching and smart guessing. The key metrics are Recall and Precision. Recall shows how many good answers the search finds out of all possible good answers. Precision shows how many found answers are actually good. We want high recall so we don't miss useful results, and high precision so results are relevant and not noisy.

Confusion Matrix for Hybrid Search Results

      | Predicted Relevant | Predicted Irrelevant |
      |--------------------|---------------------|
      | True Positive (TP)  | False Positive (FP)  |
      | False Negative (FN) | True Negative (TN)   |

      TP: Good results found
      FP: Wrong results shown
      FN: Good results missed
      TN: Correctly ignored bad results

      Total samples = TP + FP + FN + TN
    

Precision vs Recall Tradeoff with Examples

Imagine searching for a recipe. If you want to see every possible recipe (high recall), you might get many unrelated ones (lower precision). If you want only the best matches (high precision), you might miss some good recipes (lower recall). Hybrid search tries to balance this by combining exact matches (high precision) and semantic matches (high recall).

For example, in a legal document search, missing a relevant case (low recall) can be costly, so recall is more important. In a product search, showing too many unrelated items (low precision) frustrates users, so precision is key.

What Good vs Bad Metric Values Look Like

Good: Precision and recall both above 0.8 means the search finds most relevant results and keeps irrelevant ones low.

Bad: Precision below 0.5 means many wrong results show up. Recall below 0.5 means many good results are missed.

For hybrid search, a good balance is key. For example, precision = 0.85 and recall = 0.75 is usually better than precision = 0.95 but recall = 0.3.

Common Pitfalls in Hybrid Search Metrics

• Accuracy paradox: High accuracy can be misleading if most data is irrelevant. For example, if 95% of documents are irrelevant, a model that always says "irrelevant" has 95% accuracy but is useless.
• Data leakage: If test data leaks into training, metrics look better but don't reflect real performance.
• Overfitting: The search may work well on known queries but fail on new ones, showing high precision and recall only on training data.
• Ignoring user intent: Metrics don't capture if results satisfy the user's real need, so qualitative feedback is also important.

Self Check: Your model has 98% accuracy but 12% recall on relevant results. Is it good?

No, it is not good. The model finds very few relevant results (low recall), even if overall accuracy looks high because most data is irrelevant. This means many useful answers are missed, which defeats the purpose of search. Improving recall is critical.

Key Result

Hybrid search needs a good balance of high recall and precision to find relevant results without too much noise.