Model Evaluation - LangChain in Production

Learn how to evaluate, benchmark, and select LLMs and chains for production LangChain applications

📊 Model Evaluation Overview

Evaluating models and chains is critical for quality, reliability, and cost. This guide covers evaluation metrics, benchmarking, A/B testing, and continuous improvement.

---

📈 Evaluation Metrics

• Accuracy: Correctness of outputs
• Latency: Response time
• Cost: Token usage, API spend
• Robustness: Handling edge cases, adversarial inputs
• User Satisfaction: Feedback, ratings

---

🧪 Benchmarking & A/B Testing

• Compare models and chains on real-world tasks
• Use A/B tests to select best-performing configurations
• Automate benchmarking in CI/CD pipelines

---

🛠️ Example: Model Evaluation Script

from langchain_openai import ChatOpenAI
import time

def evaluate_model(prompt, expected):
    llm = ChatOpenAI(model="gpt-3.5-turbo")
    start = time.time()
    response = llm.invoke(prompt)
    latency = time.time() - start
    accuracy = response.strip().lower() == expected.strip().lower()
    print(f"Latency: {latency:.2f}s, Accuracy: {accuracy}")

evaluate_model("What is the capital of France?", "Paris")

---

🔄 Continuous Improvement

• Track evaluation metrics over time
• Collect user feedback and iterate
• Update models and chains based on results

---

🔗 Next Steps

• Continuous Improvement
• User Experience
• Accessibility

---

Key Model Evaluation Takeaways:

• Use metrics to guide model and chain selection
• Benchmark and A/B test configurations
• Automate evaluation and improvement
• Collect feedback and iterate
• Continuously monitor and optimize models