Why Custom evaluation metrics in LangChain? - Purpose & Use Cases

The Big Idea

Discover how custom metrics turn vague guesses into clear, actionable insights for your AI models!

The Scenario

Imagine you built a language model app and want to check how well it answers questions. You try to judge its quality by just counting correct answers manually or using a simple score.

The Problem

Manual checking is slow and tiring. Simple scores miss important details like answer relevance or style. You can't easily compare models or improve them without clear, tailored feedback.

The Solution

Custom evaluation metrics let you define exactly how to measure your model's performance. You can capture what really matters for your app, like accuracy, relevance, or creativity, automatically and consistently.

Before vs After

✗ Before

score = sum([1 if ans == correct else 0 for ans in answers])

✓ After

metric = CustomMetric(relevance_weight=0.7, style_weight=0.3)
score = metric.evaluate(predictions, references)

What It Enables

It enables precise, automated feedback tailored to your app's unique goals, helping you improve models faster and smarter.

Real Life Example

For a chatbot helping customers, a custom metric can measure not just correct info but also politeness and helpfulness, ensuring a better user experience.

Key Takeaways

Manual evaluation is slow and misses key quality aspects.

Custom metrics automate and tailor performance measurement.

This leads to smarter improvements and better app results.

Practice

1. What is the main purpose of creating a custom evaluation metric in Langchain?

easy

A. To speed up the AI model training process

B. To measure AI results in a way that fits your specific needs

C. To automatically fix errors in AI outputs

D. To replace the AI model with a simpler one

Solution

1. Step 1: Understand the role of evaluation metrics

   Evaluation metrics measure how well an AI model performs its task.

2. Step 2: Identify why custom metrics are used

   Custom metrics let you measure results in ways that standard metrics might not cover, fitting your unique needs.

3. Final Answer:

   To measure AI results in a way that fits your specific needs -> Option B

4. Quick Check:

   Custom metrics = tailored measurement [OK]

Hint: Custom metrics tailor scoring to your AI task [OK]

Common Mistakes:

• Thinking custom metrics speed training
• Believing they fix AI errors automatically
• Confusing metrics with model replacement

2. Which of the following is the correct way to start defining a custom evaluation metric class in Langchain?

easy

A. class MyMetric(Evaluation):

B. def MyMetric():

C. class MyMetric():

D. function MyMetric extends Evaluation {}

Solution

1. Step 1: Recall Langchain class inheritance syntax

   Custom metrics inherit from the Evaluation base class using Python class syntax.

2. Step 2: Identify correct class definition

   class MyMetric(Evaluation): correctly defines a class inheriting from Evaluation, matching Langchain patterns.

3. Final Answer:

   class MyMetric(Evaluation): -> Option A

4. Quick Check:

   Class inherits Evaluation = correct syntax [OK]

Hint: Use class inheritance with Evaluation base [OK]

Common Mistakes:

• Defining a function instead of a class
• Missing inheritance from Evaluation
• Using JavaScript syntax in Python

3. Given this custom metric class, what will metric.evaluate(['hello'], ['hello']) return?

class ExactMatch(Evaluation):
    def evaluate(self, predictions, references):
        return sum(p == r for p, r in zip(predictions, references)) / len(references)

medium

A. 1.0

B. 0.0

C. Error due to missing method

D. None

Solution

1. Step 1: Understand the evaluate method logic

   It compares each prediction to the reference and counts matches, then divides by total references.

2. Step 2: Apply inputs to the method

   With predictions=['hello'] and references=['hello'], the single pair matches, so sum is 1 and length is 1, result is 1/1 = 1.0.

3. Final Answer:

   1.0 -> Option A

4. Quick Check:

   Exact match count / total = 1.0 [OK]

Hint: Check if predictions equal references, then divide [OK]

Common Mistakes:

• Forgetting to divide by length
• Confusing sum with boolean values
• Expecting method to return a list

4. What is wrong with this custom metric class that causes an error?

class LengthDiff(Evaluation):
    def evaluate(self, predictions, references):
        return abs(len(predictions) - len(references)) / len(references)

medium

A. It returns a number instead of a score between 0 and 1

B. It does not implement the evaluate method

C. It uses abs() incorrectly causing a syntax error

D. It does not handle empty lists causing runtime error

Solution

1. Step 1: Analyze the evaluate method with empty references

   If references=[], len(references)=0 causes ZeroDivisionError in the division.

2. Step 2: Identify the runtime error cause

   The code divides by len(references) without checking if references is empty, causing runtime error.

3. Final Answer:

   It does not handle empty lists causing runtime error -> Option D

4. Quick Check:

   len(references)==0 -> ZeroDivisionError [OK]

Hint: Check how method handles empty input lists [OK]

Common Mistakes:

• Assuming abs() causes syntax error
• Thinking evaluate method is missing
• Ignoring empty list edge cases

5. You want to create a custom metric that scores AI answers higher if they contain more keywords from a reference list. Which approach fits best?

hard

A. Calculate the difference in length between prediction and reference

B. Check if prediction exactly matches the reference string

C. Count how many keywords appear in the prediction, divide by total keywords

D. Return a fixed score regardless of prediction content

Solution

1. Step 1: Understand the goal of keyword-based scoring

   The metric should reward predictions containing more keywords from the reference list.

2. Step 2: Identify the approach that measures keyword presence proportionally

   Counting keywords in prediction and dividing by total keywords gives a score reflecting keyword coverage.

3. Final Answer:

   Count how many keywords appear in the prediction, divide by total keywords -> Option C

4. Quick Check:

   Keyword coverage scoring = Count how many keywords appear in the prediction, divide by total keywords [OK]

Hint: Score by keyword matches divided by total keywords [OK]

Common Mistakes:

• Using exact match instead of keyword count
• Measuring length difference unrelated to keywords
• Returning fixed scores ignoring content