Confidence Scoring for Responses

In many production environments (like healthcare or finance), giving an "Unsure" answer is much safer than giving a "Confident but Wrong" one. Confidence Scoring helps you flag risky outputs for human review.

Three Tiers of Confidence

1. Retrieval Confidence

Based on the Cosine Similarity score from your vector database.

• If the top doc has a score of 0.95, confidence is high.
• If the top doc has a score of 0.65, the system might be "guessing."

2. Semantic Log-Probs (Advanced)

Some LLM APIs provide "Log-probabilities" for every token generated. If the probabilities are low, the model is uncertain about its word choice.

3. LLM Self-Assessment

Ask the model to rate its own confidence:

"On a scale of 1-5, how confident are you that the above answer is fully supported by the context? Provide a reason for your score."

Implementing a Threshold

def handle_response(response, confidence_score):
    if confidence_score < 3:
        return "I'm not confident in this answer. Would you like me to search other sources?"
    return response

Consensus Scoring (Ensemble RAG)

Run the same query 3 times with different retrieved chunks.

• If all 3 answers are identical → High Confidence.
• If all 3 answers are different → Low Confidence / Hallucination Risk.

Visualization for Users

Don't just show a number (0.87). Use qualitative labels:

• 🟢 Verified: Clearly supported by docs.
• 🟡 Partial: Some details missing from context.
• 🔴 Uncertain: System is making a logical leap.

Exercises

1. Compare "Self-rated" confidence to actual "Accuracy" scores. Do they always match? (Hint: Models are often over-confident).
2. Why is "Retrieved Similarity" a poor proxy for "Answer Accuracy"?
3. How would you calculate confidence for a "Visual" search?