Confidence Scoring in LangChain for Enhanced AI Output Reliability

Introduction

Ensuring the reliability of AI-driven applications is critical for delivering trustworthy and actionable outputs. LangChain, a versatile framework for building applications powered by large language models (LLMs), supports confidence scoring to quantify the certainty or reliability of model outputs, such as responses from chains, agents, or retrievers. Accessible under the /langchain/evaluation/confidence-scoring path, confidence scoring enables developers to evaluate and prioritize outputs based on their estimated reliability, enhancing decision-making and user trust. This comprehensive guide explores confidence scoring in LangChain, covering setup, core techniques, best practices, practical applications, and advanced configurations, empowering developers to integrate confidence metrics into their AI systems effectively.

To understand LangChain’s broader evaluation ecosystem, start with LangChain Evaluation Introduction.

What is Confidence Scoring in LangChain?

Confidence scoring in LangChain involves assigning a numerical score (typically between 0 and 1) to model outputs to indicate their estimated reliability or certainty. This score can reflect the model’s confidence in its response, the quality of retrieved documents, or the coherence of generated text. Confidence scoring is not a built-in feature of LangChain but can be implemented using the langchain.evaluation module, custom evaluators, or LLM-based judgments, often integrated with LangSmith for dataset-driven analysis. It is particularly valuable for applications requiring high reliability, such as question answering, decision support, or automated workflows, where uncertain outputs can be flagged for review or alternative handling.

For related concepts, see LangChain Metrics Overview and Evaluating Retrieval.

Why Use Confidence Scoring?

Confidence scoring is essential for:

• Reliability Assessment: Quantify the trustworthiness of model outputs.
• Decision Support: Prioritize high-confidence responses or flag low-confidence ones for review.
• User Trust: Provide transparency by indicating output certainty.
• Error Mitigation: Identify and handle uncertain or ambiguous responses effectively.

Explore evaluation capabilities at the LangChain Evaluation Documentation.

Setting Up Confidence Scoring

To implement confidence scoring in LangChain, you need to install the required packages, configure your pipeline (e.g., chain or agent), define confidence scoring logic, and integrate with LangSmith for evaluation. Below is a setup for adding confidence scoring to a RetrievalQA pipeline using a custom LLM-based confidence evaluator:

from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.documents import Document
from langchain.evaluation import StringEvaluator
from langchain.chains import RetrievalQA
from langchain_core.prompts import PromptTemplate
from langsmith import Client
from langsmith.evaluation import evaluate
import os
import logging
import time
from typing import Dict, Any

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Set LangSmith environment variables
os.environ["LANGSMITH_API_KEY"] = ""
os.environ["LANGSMITH_PROJECT"] = "confidence-scoring"

# Initialize embeddings and language model
embedding_function = OpenAIEmbeddings(model="text-embedding-3-small")
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

# Create sample documents
documents = [
    Document(page_content="The capital of France is Paris.", metadata={"source": "geo"}),
    Document(page_content="The Eiffel Tower is in Paris.", metadata={"source": "landmark"}),
    Document(page_content="Florida is a state in the USA.", metadata={"source": "geo"})
]

# Initialize Chroma vector store
vector_store = Chroma.from_documents(
    documents,
    embedding_function,
    collection_name="langchain_example",
    persist_directory="./chroma_db"
)

# Set up RetrievalQA pipeline
prompt = PromptTemplate.from_template("Answer: {question}")
qa_pipeline = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vector_store.as_retriever(search_kwargs={"k": 2}),
    return_source_documents=True  # Return retrieved documents for confidence scoring
)

# Initialize LangSmith client
client = Client()

# Create or load a dataset
dataset_name = "qa_confidence_dataset"
try:
    dataset = client.create_dataset(dataset_name=dataset_name)
except:
    dataset = client.get_dataset(dataset_name=dataset_name)

# Add examples to dataset
examples = [
    {
        "input": "What is the capital of France?",
        "output": "Paris",
        "expected_docs": ["The capital of France is Paris."]
    },
    {
        "input": "Where is the Eiffel Tower?",
        "output": "Paris",
        "expected_docs": ["The Eiffel Tower is in Paris."]
    },
    {
        "input": "What is the capital of Florida?",
        "output": "",
        "expected_docs": ["Florida is a state in the USA."]
    }
]
for example in examples:
    client.create_example(
        inputs={"question": example["input"]},
        outputs={"answer": example["output"], "expected_docs": example["expected_docs"]},
        dataset_id=dataset.id
    )

# Define custom confidence evaluator
class ConfidenceEvaluator(StringEvaluator):
    """Evaluates the confidence of a response based on document relevance and clarity."""
    def __init__(self, llm):
        self.llm = llm

    def _evaluate_strings(self, prediction: str, input: str = None, retrieved_docs: list = None, **kwargs) -> Dict[str, Any]:
        from langchain.evaluation import load_evaluator, EvaluatorType
        # Base confidence on relevance and clarity
        relevance_evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="relevance", llm=self.llm)
        clarity_evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="clarity", llm=self.llm)

        relevance_score = relevance_evaluator.evaluate_strings(prediction=prediction, input=input)["score"]
        clarity_score = clarity_evaluator.evaluate_strings(prediction=prediction, input=input)["score"]

        # Adjust confidence based on retrieved documents (if available)
        doc_score = 1.0
        if retrieved_docs:
            doc_relevance = relevance_evaluator.evaluate_strings(
                prediction=retrieved_docs[0].page_content if retrieved_docs else "",
                input=input
            )["score"]
            doc_score = doc_relevance * 0.5  # Weight document relevance

        # Combine scores (weighted average)
        confidence = (relevance_score * 0.4 + clarity_score * 0.4 + doc_score * 0.2)
        return {
            "key": "confidence",
            "score": confidence,
            "comment": f"Confidence based on relevance ({relevance_score}), clarity ({clarity_score}), and document relevance ({doc_score})."
        }

# Define evaluation functions
def evaluate_correctness(run, example) -> Dict[str, Any]:
    prediction = run.outputs.get("result", "")
    reference = example.outputs.get("answer", "")
    question = example.inputs.get("question", "")
    if not reference:
        return {"key": "correctness", "score": None, "comment": "No reference provided."}
    evaluator = load_evaluator(EvaluatorType.QA, llm=llm)
    result = evaluator.evaluate_strings(
        prediction=prediction,
        reference=reference,
        input=question
    )
    return {"key": "correctness", "score": result["score"], "comment": result.get("reasoning", "")}

def evaluate_confidence(run, example) -> Dict[str, Any]:
    prediction = run.outputs.get("result", "")
    question = example.inputs.get("question", "")
    retrieved_docs = run.outputs.get("source_documents", [])
    evaluator = ConfidenceEvaluator(llm=llm)
    result = evaluator.evaluate_strings(
        prediction=prediction,
        input=question,
        retrieved_docs=retrieved_docs
    )
    return result

# Run evaluation
start_time = time.time()
results = evaluate(
    lambda inputs: qa_pipeline.invoke({"query": inputs["question"]}),
    data=dataset_name,
    evaluators=[evaluate_correctness, evaluate_confidence],
    experiment_prefix="confidence_scoring_test",
    metadata={"version": "1.0", "evaluated_at": "2025-05-15T15:30:00Z"}
)

# Log results
logger.info(f"Evaluation completed in {time.time() - start_time:.2f} seconds")
print(f"Test Results: {results}")
print("View detailed results in LangSmith dashboard under 'confidence_scoring_test' experiment.")

This setup creates a RetrievalQA pipeline, uploads a test dataset to LangSmith, and evaluates outputs for correctness and a custom confidence score based on relevance, clarity, and document quality. Results are logged in the LangSmith dashboard.

Installation

Install the core packages for LangChain, LangSmith, and evaluation:

pip install langchain langchain-chroma langchain-openai chromadb langsmith

For specific metrics, install additional dependencies:

• NLP Metrics: pip install nltk rouge-score for BLEU/ROUGE scores.
• Embedding Metrics: Included with langchain-openai.

Example:

pip install nltk rouge-score

For detailed installation guidance, see LangSmith Documentation.

Configuration Options

Customize confidence scoring during setup:

• Pipeline Configuration:
• Include components that provide metadata (e.g., retrieved documents) for confidence scoring.
• Example:

• qa_pipeline = RetrievalQA.from_chain_type(..., return_source_documents=True)

• Dataset Configuration:
• Include queries with expected outputs or documents for validation.
• Example:

• client.create_example(
          inputs={"question": "What is the capital of France?"},
          outputs={"answer": "Paris", "expected_docs": ["The capital of France is Paris."]},
          dataset_id=dataset.id
      )

• Confidence Evaluator:
• Define custom logic combining relevance, clarity, or document quality.
• Example:

• class ConfidenceEvaluator(StringEvaluator):
          def _evaluate_strings(self, prediction: str, input: str = None, **kwargs) -> Dict[str, Any]:
              score = 0.9  # Placeholder logic
              return {"key": "confidence", "score": score, "comment": "Example confidence score."}

• LangSmith Integration:
• Track experiments with metadata for analysis.
• Example:

• evaluate(..., metadata={"version": "1.0", "pipeline": "RetrievalQA"})

Core Techniques for Confidence Scoring

1. LLM-Based Confidence Scoring

Use an LLM to estimate confidence based on output quality metrics.

• Criteria-Based Confidence:
• Combine relevance and clarity scores to compute confidence.
• Example (as shown in ConfidenceEvaluator above):

• relevance_score = relevance_evaluator.evaluate_strings(prediction=prediction, input=input)["score"]
      clarity_score = clarity_evaluator.evaluate_strings(prediction=prediction, input=input)["score"]
      confidence = (relevance_score * 0.5 + clarity_score * 0.5)

• Custom Criteria:
• Define project-specific criteria (e.g., “confidence in factual accuracy”).
• Example:

• def evaluate_factual_confidence(run, example):
          prediction = run.outputs.get("result", "")
          question = example.inputs.get("question", "")
          evaluator = load_evaluator(
              EvaluatorType.CRITERIA,
              criteria={"factual_confidence": "How confident is the response in its factual accuracy?"},
              llm=llm
          )
          result = evaluator.evaluate_strings(prediction=prediction, input=question)
          return {
              "key": "factual_confidence",
              "score": result["score"],
              "comment": result.get("reasoning", "")
          }

2. Document-Based Confidence Scoring

Leverage retrieved document quality to inform confidence scores.

• Document Relevance:
• Score confidence based on the relevance of retrieved documents.
• Example:

• def evaluate_doc_confidence(run, example):
          retrieved_docs = run.outputs.get("source_documents", [])
          question = example.inputs.get("question", "")
          if not retrieved_docs:
              return {"key": "doc_confidence", "score": 0.0, "comment": "No documents retrieved."}
          prediction = retrieved_docs[0].page_content
          evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="relevance", llm=llm)
          result = evaluator.evaluate_strings(prediction=prediction, input=question)
          return {
              "key": "doc_confidence",
              "score": result["score"],
              "comment": result.get("reasoning", "")
          }

• Document Coverage:
• Assess confidence based on whether retrieved documents cover all necessary information.
• Example:

• def evaluate_doc_coverage(run, example):
          retrieved_docs = run.outputs.get("source_documents", [])
          expected_docs = example.outputs.get("expected_docs", [])
          if not retrieved_docs or not expected_docs:
              return {"key": "doc_coverage", "score": 0.0, "comment": "Missing documents or references."}
          retrieved_contents = [doc.page_content for doc in retrieved_docs]
          coverage = sum(1 for doc in expected_docs if doc in retrieved_contents) / len(expected_docs)
          return {
              "key": "doc_coverage",
              "score": coverage,
              "comment": f"{sum(1 for doc in expected_docs if doc in retrieved_contents)}/{len(expected_docs)} expected documents covered."
          }

3. Rule-Based Confidence Scoring

Use deterministic logic to compute confidence scores based on output characteristics.

• Length-Based Confidence:
• Assign higher confidence to concise yet informative responses.
• Example:

• def evaluate_length_confidence(run, example):
          prediction = run.outputs.get("result", "")
          word_count = len(prediction.split())
          score = 1.0 if 10 <= word_count <= 50 else 0.7
          return {
              "key": "length_confidence",
              "score": score,
              "comment": f"Word count ({word_count}) {'within' if score == 1.0 else 'outside'} optimal range (10-50)."
          }

4. Pairwise Confidence Comparison

Compare confidence scores of two outputs to prioritize the more reliable one.

• Pairwise Evaluator:
• Uses an LLM to determine which output has higher confidence.
• Example:

• def evaluate_pairwise_confidence(run, example):
          prediction = run.outputs.get("result", "")
          prediction_b = "Alternative response"  # Placeholder
          question = example.inputs.get("question", "")
          evaluator = load_evaluator(EvaluatorType.PAIRWISE_STRING, llm=llm)
          result = evaluator.evaluate_string_pairs(
              prediction=prediction,
              prediction_b=prediction_b,
              input=question,
              criteria={"confidence": "Which response appears more confident and reliable?"}
          )
          return {
              "key": "pairwise_confidence",
              "score": result["score"],
              "comment": result.get("reasoning", "")
          }

5. Human-in-the-Loop Validation

Supplement automated confidence scoring with human feedback for subjective assessment.

• LangSmith HITL:
• Use LangSmith to collect human feedback on confidence scores.
• Example: In LangSmith UI, reviewers validate confidence scores (0-1) with comments like “Confidence score seems high but response lacks detail.”

Comprehensive Example

Here’s a complete system evaluating a LangChain agent pipeline with confidence scoring and other metrics using LangSmith, integrated with Chroma and MongoDB Atlas:

from langchain_chroma import Chroma
from langchain_mongodb import MongoDBAtlasVectorSearch
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.documents import Document
from langchain.evaluation import load_evaluator, EvaluatorType, StringEvaluator
from langchain.agents import initialize_agent, AgentType
from langchain.tools import Tool
from langchain_community.tools import DuckDuckGoSearchRun
from langsmith import Client
from langsmith.evaluation import evaluate
from pymongo import MongoClient
import os
import logging
import time
from typing import Dict, Any

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Set LangSmith environment variables
os.environ["LANGSMITH_API_KEY"] = ""
os.environ["LANGSMITH_PROJECT"] = "agent-confidence-scoring"

# Initialize embeddings and language model
embedding_function = OpenAIEmbeddings(model="text-embedding-3-small")
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

# Create sample documents
documents = [
    Document(page_content="The capital of France is Paris.", metadata={"source": "geo"}),
    Document(page_content="The Eiffel Tower is in Paris.", metadata={"source": "landmark"}),
    Document(page_content="Florida is a state in the USA.", metadata={"source": "geo"})
]

# Initialize Chroma and MongoDB Atlas vector stores
chroma_store = Chroma.from_documents(
    documents,
    embedding_function,
    collection_name="langchain_example",
    persist_directory="./chroma_db"
)
client = MongoClient("mongodb+srv://:@.mongodb.net/")
collection = client["langchain_db"]["example_collection"]
mongo_store = MongoDBAtlasVectorSearch.from_documents(
    documents,
    embedding_function,
    collection=collection,
    index_name="vector_index"
)

# Set up search tool and agent pipeline
search = DuckDuckGoSearchRun()
tools = [
    Tool(
        name="Search",
        func=search.run,
        description="Useful for answering questions about recent events or general knowledge."
    )
]
agent = initialize_agent(
    tools=tools,
    llm=llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)

# Initialize LangSmith client
ls_client = Client()

# Create or load dataset
dataset_name = "agent_confidence_dataset"
try:
    dataset = ls_client.create_dataset(dataset_name=dataset_name)
except:
    dataset = ls_client.get_dataset(dataset_name=dataset_name)

# Add examples to dataset
examples = [
    {
        "input": "What is the capital of France?",
        "output": "Paris",
        "expected_docs": ["The capital of France is Paris."]
    },
    {
        "input": "Where is the Eiffel Tower?",
        "output": "Paris",
        "expected_docs": ["The Eiffel Tower is in Paris."]
    },
    {
        "input": "What is the capital of Florida?",
        "output": "",
        "expected_docs": ["Florida is a state in the USA."]
    }
]
for example in examples:
    ls_client.create_example(
        inputs={"question": example["input"]},
        outputs={"answer": example["output"], "expected_docs": example["expected_docs"]},
        dataset_id=dataset.id
    )

# Define custom confidence evaluator
class ConfidenceEvaluator(StringEvaluator):
    def __init__(self, llm):
        self.llm = llm

    def _evaluate_strings(self, prediction: str, input: str = None, retrieved_docs: list = None, **kwargs) -> Dict[str, Any]:
        from langchain.evaluation import load_evaluator, EvaluatorType
        relevance_evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="relevance", llm=self.llm)
        clarity_evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="clarity", llm=self.llm)

        relevance_score = relevance_evaluator.evaluate_strings(prediction=prediction, input=input)["score"]
        clarity_score = clarity_evaluator.evaluate_strings(prediction=prediction, input=input)["score"]

        doc_score = 1.0
        if retrieved_docs:
            doc_relevance = relevance_evaluator.evaluate_strings(
                prediction=retrieved_docs[0].page_content if retrieved_docs else "",
                input=input
            )["score"]
            doc_score = doc_relevance * 0.5

        confidence = (relevance_score * 0.4 + clarity_score * 0.4 + doc_score * 0.2)
        return {
            "key": "confidence",
            "score": confidence,
            "comment": f"Confidence based on relevance ({relevance_score}), clarity ({clarity_score}), and document relevance ({doc_score})."
        }

# Define evaluation functions
def evaluate_correctness(run, example) -> Dict[str, Any]:
    prediction = run.outputs.get("output", "")
    reference = example.outputs.get("answer", "")
    question = example.inputs.get("question", "")
    if not reference:
        return {"key": "correctness", "score": None, "comment": "No reference provided."}
    evaluator = load_evaluator(EvaluatorType.QA, llm=llm)
    result = evaluator.evaluate_strings(
        prediction=prediction,
        reference=reference,
        input=question
    )
    return {"key": "correctness", "score": result["score"], "comment": result.get("reasoning", "")}

def evaluate_confidence(run, example) -> Dict[str, Any]:
    prediction = run.outputs.get("output", "")
    question = example.inputs.get("question", "")
    retrieved_docs = run.outputs.get("source_documents", [])
    evaluator = ConfidenceEvaluator(llm=llm)
    result = evaluator.evaluate_strings(
        prediction=prediction,
        input=question,
        retrieved_docs=retrieved_docs
    )
    return result

def evaluate_doc_coverage(run, example) -> Dict[str, Any]:
    retrieved_docs = run.outputs.get("source_documents", [])
    expected_docs = example.outputs.get("expected_docs", [])
    if not retrieved_docs or not expected_docs:
        return {"key": "doc_coverage", "score": 0.0, "comment": "Missing documents or references."}
    retrieved_contents = [doc.page_content for doc in retrieved_docs]
    coverage = sum(1 for doc in expected_docs if doc in retrieved_contents) / len(expected_docs)
    return {
        "key": "doc_coverage",
        "score": coverage,
        "comment": f"{sum(1 for doc in expected_docs if doc in retrieved_contents)}/{len(expected_docs)} expected documents covered."
    }

# Run evaluation
start_time = time.time()
results = evaluate(
    lambda inputs: {
        "output": agent.run(inputs["question"]),
        "source_documents": qa_pipeline.invoke({"query": inputs["question"]})["source_documents"]
    },
    data=dataset_name,
    evaluators=[evaluate_correctness, evaluate_confidence, evaluate_doc_coverage],
    experiment_prefix="agent_confidence_test",
    metadata={"version": "1.0", "evaluated_at": "2025-05-15T15:30:00Z"}
)

# Log results
logger.info(f"Evaluation completed in {time.time() - start_time:.2f} seconds")
print(f"Test Results: {results}")
print("View detailed results in LangSmith dashboard under 'agent_confidence_test' experiment.")

Output:

Evaluation completed in 10.45 seconds
Test Results: 
View detailed results in LangSmith dashboard under 'agent_confidence_test' experiment.

The evaluation tests the agent pipeline for correctness, confidence (custom metric), and document coverage, logging results in LangSmith for detailed analysis.

Best Practices

1. Define Clear Confidence Criteria: Base confidence on measurable qualities like relevance, clarity, or document quality.
2. Combine Metrics: Pair confidence scores with correctness and coverage for comprehensive evaluation.
3. Use Diverse Datasets: Include varied inputs and edge cases to test confidence robustness.
4. Integrate with LangSmith: Leverage LangSmith for dataset management, tracking, and visualization.
5. Validate Scores: Cross-check confidence scores with human feedback for critical applications.
6. Optimize LLM Costs: Use cost-effective LLMs (e.g., gpt-3.5-turbo) for evaluation.

Error Handling

• Missing Documents: Handle cases with no retrieved documents by assigning low confidence scores.
• LLM Failures: Implement retries or fallback models for evaluation errors.
• Dataset Issues: Validate dataset format to avoid parsing errors.
• Resource Limits: Batch evaluations to manage API costs and rate limits.

See Troubleshooting.

Limitations

• LLM Bias: Confidence scores based on LLM judgments may vary by model or prompt.
• Subjectivity: Scores for subjective qualities like clarity depend on LLM interpretation.
• Cost: LLM-based evaluations can be expensive for large datasets.
• Ground Truth Dependency: Correctness metrics require accurate references.

Recent Developments

• 2025 Updates: LangSmith introduced confidence scoring templates for easier metric creation.
• Community Feedback: X posts highlight confidence scoring for RAG systems in customer support, emphasizing reliability.
• LangSmith UI: Enhanced visualization for confidence score trends and experiment comparisons.

Conclusion

Confidence scoring in LangChain enhances AI output reliability by quantifying certainty, enabling developers to prioritize trustworthy responses and flag uncertain ones. By leveraging custom evaluators and LangSmith, developers can integrate confidence metrics into chains, agents, and retrievers, optimizing performance for production. Start implementing confidence scoring to improve your LangChain projects, ensuring reliable and transparent AI solutions.

For official documentation, visit LangSmith Documentation.