SmartAnswer: Enhancing AI Answering Systems with TextGrad and Test-Time Scaling

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Summary

This blog post explores how TextGrad Yuksekgonul et al., 2024 and Test-Time Scaling Jurayj et al., 2025 can be combined to create a self-improving AI answering system that:

  • Iteratively refines responses using LLM-generated textual feedback.
  • Dynamically increases compute to improve reasoning when confidence is low.
  • Avoids incorrect answers in high-stakes situations by abstaining when uncertain.

By merging these techniques, we achieve more reliable, context-aware, and confidence-calibrated AI responses.

It shows how to test this new implementation against a standard approach. It also shows how you could incorporate it into a Retrieval-Augmented Generation (RAG) system.

SmartAnswer Class Implementation

To encapsulate our approach, we define a SmartAnswer class that implements the TextGrad and Test-Time Scaling solution:

import textgrad as tg
import numpy as np

class SmartAnswer:
    def __init__(self, model_name="gpt-4o", confidence_threshold=0.95, max_iterations=3):
        self.model = tg.BlackboxLLM(model_name)
        self.confidence_threshold = confidence_threshold
        self.max_iterations = max_iterations

    def estimate_confidence(self, response):
        """
        Estimates confidence based on token probabilities using entropy-based normalization.
        """
        token_probs = np.array(response.token_probabilities)  # Assuming token_probabilities is available
        entropy = -np.sum(token_probs * np.log(token_probs))
        normalized_confidence = 1 - (entropy / np.log(len(token_probs)))  # Normalize between 0 and 1
        return normalized_confidence

    def generate_answer(self, question_text):
        question = tg.Variable(question_text, requires_grad=False)
        answer = self.model(question)
        answer.confidence = self.estimate_confidence(answer)
        
        # Apply TextGrad optimization
        loss_fn = tg.TextLoss("Evaluate the answer and suggest improvements.")
        for _ in range(self.max_iterations):
            loss = loss_fn(answer)
            loss.backward()
            optimizer = tg.TGD(parameters=[answer])
            optimizer.step()
            answer.confidence = self.estimate_confidence(answer)
        
        # Apply Test-Time Scaling
        if answer.confidence < self.confidence_threshold:
            refined_answer = self.model.compute_more_steps(question)
            refined_answer.confidence = self.estimate_confidence(refined_answer)
            if refined_answer.confidence > answer.confidence:
                answer = refined_answer
        
        # Decide whether to answer or abstain
        if answer.confidence >= self.confidence_threshold:
            return answer.value
        else:
            return "Model chooses to abstain due to low confidence."

Comparing SmartAnswer vs. Standard LLM Approach

To evaluate the benefits of SmartAnswer, we compare it against a standard LLM response generation approach:

# Standard LLM Approach
class StandardLLM:
    def __init__(self, model_name="gpt-4o"):
        self.model = tg.BlackboxLLM(model_name)
    
    def generate_answer(self, question_text):
        question = tg.Variable(question_text, requires_grad=False)
        return self.model(question).value

Testing the Two Approaches

# Initialize models
smart_answer = SmartAnswer()
standard_llm = StandardLLM()

question_text = "What is the capital of France?"

# Generate responses
smart_response = smart_answer.generate_answer(question_text)
standard_response = standard_llm.generate_answer(question_text)

print(f"SmartAnswer Response: {smart_response}")
print(f"Standard LLM Response: {standard_response}")

Expected Outcome

  • The SmartAnswer system will refine the answer iteratively and return a higher-confidence response.
  • The Standard LLM will return an answer instantly, without confidence validation or iterative improvements.
  • If SmartAnswer’s confidence threshold is not met, it may abstain, improving reliability in high-stakes applications.

Incorporating SmartAnswer into a RAG-Based System

A Retrieval-Augmented Generation (RAG) system enhances LLM responses by retrieving relevant documents before generating an answer. SmartAnswer can be seamlessly integrated into a RAG pipeline to refine responses iteratively while ensuring confidence-based answer selection.

Steps to Integrate SmartAnswer with RAG

  1. Retrieve relevant documents based on the query.
  2. Generate an initial response using SmartAnswer.
  3. Refine the response iteratively with TextGrad.
  4. Use confidence-based Test-Time Scaling to decide whether to answer or abstain.

Example Implementation

from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA

class RAGSmartAnswer:
    def __init__(self, retriever, model_name="gpt-4o"):
        self.retriever = retriever
        self.smart_answer = SmartAnswer(model_name)
    
    def get_answer(self, query):
        retrieved_docs = self.retriever.get_relevant_documents(query)
        context = "\n".join([doc.page_content for doc in retrieved_docs])
        combined_query = f"Context: {context}\nQuestion: {query}"
        return self.smart_answer.generate_answer(combined_query)

Testing RAG with SmartAnswer

# Initialize a vector database retriever
retriever = FAISS.load_local("./vectorstore", OpenAIEmbeddings())
rag_smart_answer = RAGSmartAnswer(retriever)

query = "What are the benefits of Test-Time Scaling?"
response = rag_smart_answer.get_answer(query)
print(f"RAG-SmartAnswer Response: {response}")

Benefits of SmartAnswer in RAG

  • Ensures retrieved context is properly leveraged before generating an answer.
  • Improves response accuracy through iterative refinement.
  • Minimizes hallucinations by abstaining if the confidence is low.
  • Optimally scales compute based on confidence scores.

Conclusion

By implementing SmartAnswer, we create an AI system that:

  • Refines responses iteratively using TextGrad.
  • Applies confidence-based test-time scaling to improve reasoning.
  • Avoids incorrect answers by abstaining when confidence is low.
  • Integrates seamlessly into RAG pipelines to improve answer relevance and accuracy.

This approach ensures that AI-generated answers are more accurate, context-aware, and reliable, making it ideal for knowledge-intensive and high-stakes applications.

References

  • TextGrad: Automatic ‘Differentiation’ via Text - Yuksekgonul et al., 2024.
  • Is That Your Final Answer? Test-Time Scaling Improves Selective Question Answering - Jurayj et al., 2025.