Why evaluation prevents production failures in LangChain - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an evaluation that checks if the output matches the expected answer.

LangChain

from langchain.evaluation import StringEvaluator

evaluator = StringEvaluator()
result = evaluator.evaluate(output=[1], reference="Hello World")

Drag options to blanks, or click blank then click option'

Aoutput_text

Bresponse

Coutput

Dinput_text

Attempts:

3 left

💡 Hint

Common Mistakes

Using the input variable instead of the output.

Passing a variable that is not defined.

The evaluate method requires the actual output to compare with the reference. Here, output is the correct variable holding the generated result.

2fill in blank

medium

Complete the code to run an evaluation on a chain's output using LangChain's evaluation tools.

LangChain

from langchain.chains import LLMChain
from langchain.evaluation import StringEvaluator

chain = LLMChain(llm=llm, prompt=prompt)
output = chain.run("What is 2 + 2?")
evaluator = StringEvaluator()
score = evaluator.evaluate(output=[1], reference="4")

Drag options to blanks, or click blank then click option'

Aoutput

Bresult

Cresponse

Danswer

Attempts:

3 left

💡 Hint

Common Mistakes

Passing an undefined variable to the evaluator.

Using the input prompt instead of the output.

The variable output holds the chain's generated answer, which is what the evaluator needs to check against the reference.

3fill in blank

hard

Fix the error in the evaluation code by completing the blank with the correct method to get the chain's output.

LangChain

output = chain.[1]("Calculate 5 times 3")
score = evaluator.evaluate(output=output, reference="15")

Drag options to blanks, or click blank then click option'

Arun

Bexecute

Ccall

Dprocess

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent methods like execute or process.

Confusing call with run.

The run method is the correct way to execute the chain and get its output as a string.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that evaluates outputs only if the score is above a threshold.

LangChain

results = {output: evaluator.evaluate(output=output, reference=ref) for output, ref in outputs.items() if evaluator.evaluate(output=output, reference=ref) [1] [2]

Drag options to blanks, or click blank then click option'

A>

B==

C0.8

D0.5

Attempts:

3 left

💡 Hint

Common Mistakes

Using equality instead of greater than.

Choosing a threshold too low to be meaningful.

The comprehension filters results where the evaluation score is greater than 0.8, meaning only good matches are included.

5fill in blank

hard

Fill all three blanks to define a function that evaluates a list of outputs and returns those with scores above a threshold.

LangChain

def filter_good_outputs(outputs, evaluator, threshold):
    return {output: score for output in outputs if (score := evaluator.evaluate(output=output, reference=outputs[output])) [1] threshold and score [2] 1 and output [3] outputs}

Drag options to blanks, or click blank then click option'

A>

B<=

Cin

Dnot in

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators that invert logic.

Checking if output is not in outputs instead of in outputs.

The function keeps outputs with scores greater than the threshold and less than or equal to 1, ensuring valid scores. It also checks that the output is a key in the outputs dictionary.

Practice

1. Why is evaluation important before deploying a LangChain application to production?

easy

A. It automatically updates the application without manual work.

B. It makes the code run faster in production.

C. It reduces the size of the application files.

D. It helps catch errors early to avoid failures in real use.

Solution

1. Step 1: Understand the purpose of evaluation

   Evaluation tests the code output before real use to find errors early.

2. Step 2: Connect evaluation to production reliability

   By catching errors early, evaluation prevents failures when users interact with the app.

3. Final Answer:

   It helps catch errors early to avoid failures in real use. -> Option D

4. Quick Check:

   Evaluation prevents failures = C [OK]

Hint: Evaluation finds bugs before users see them [OK]

Common Mistakes:

• Thinking evaluation speeds up code
• Believing evaluation auto-updates apps
• Confusing evaluation with file size reduction

2. Which of the following is the correct way to run an evaluation on a LangChain chain object named my_chain?

easy

A. my_chain.evaluate_chain()

B. my_chain.run_evaluation()

C. my_chain.evaluate()

D. my_chain.eval()

Solution

1. Step 1: Recall LangChain evaluation method

   The standard method to evaluate a chain is evaluate().

2. Step 2: Check other options for correctness

   Other method names like run_evaluation(), evaluate_chain(), or eval() are not valid LangChain methods.

3. Final Answer:

   my_chain.evaluate() -> Option C

4. Quick Check:

   Correct evaluation method = A [OK]

Hint: Use exact method names from docs [OK]

Common Mistakes:

• Guessing method names without checking docs
• Using shortened or incorrect method names
• Confusing evaluation with running the chain

3. Consider this code snippet:

result = my_chain.evaluate(input_data={'text': 'Hello'})
print(result)

What will this code output if my_chain has a bug causing it to return None instead of a string?

medium

A. It prints None indicating a problem.

B. It prints the expected string output.

C. It raises a syntax error.

D. It crashes with a runtime exception.

Solution

1. Step 1: Understand the evaluate method output

   The evaluate method returns the chain's output or None if there's a bug.

2. Step 2: Analyze the print statement behavior

   Printing None will display the word None in the console, not an error.

3. Final Answer:

   It prints None indicating a problem. -> Option A

4. Quick Check:

   Bug causes None output = A [OK]

Hint: Print output to check for None or errors [OK]

Common Mistakes:

• Expecting a syntax error from None
• Assuming it crashes instead of returning None
• Thinking it prints the correct string despite bug

4. You run this code to evaluate a LangChain chain:

result = my_chain.evaluate(input_data={'text': 'Test'})
print(result)

But you get a TypeError saying evaluate() got an unexpected keyword argument 'input_data'. What is the likely cause?

medium

A. The my_chain object is not a LangChain chain.

B. The evaluate method does not accept input_data as a parameter.

C. You forgot to import the evaluate function.

D. The print statement is incorrect.

Solution

1. Step 1: Analyze the error message

   The error says evaluate() got an unexpected keyword argument input_data, meaning this argument is invalid.

2. Step 2: Understand method parameters

   The evaluate method expects inputs differently, not as input_data. Passing unknown keywords causes this error.

3. Final Answer:

   The evaluate method does not accept input_data as a parameter. -> Option B

4. Quick Check:

   Wrong parameter name causes TypeError = B [OK]

Hint: Check method parameters carefully in docs [OK]

Common Mistakes:

• Assuming object type is wrong without checking
• Blaming missing imports for parameter errors
• Thinking print causes TypeError

5. You want to prevent production failures by evaluating a LangChain chain that processes user queries. Which approach best improves reliability?

hard

A. Continuously evaluate with test inputs and update the chain before production.

B. Skip evaluation and fix errors only when users report them.

C. Evaluate only on random inputs without reviewing results.

D. Run evaluation only once after deployment to check output.

Solution

1. Step 1: Understand continuous evaluation benefits

   Evaluating continuously with test inputs helps catch new errors and improve the chain before users see problems.

2. Step 2: Compare other options

   Running evaluation once or skipping it delays error detection. Random inputs without review do not ensure reliability.

3. Final Answer:

   Continuously evaluate with test inputs and update the chain before production. -> Option A

4. Quick Check:

   Continuous evaluation improves reliability = D [OK]

Hint: Test often with real-like inputs before release [OK]

Common Mistakes:

• Thinking one-time evaluation is enough
• Ignoring errors until users report them
• Evaluating without checking results