Automated evaluation pipelines in LangChain - Step-by-Step Execution

Concept Flow - Automated evaluation pipelines

Define evaluation criteria

↓

Prepare input data

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Run model with input

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Collect model output

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Apply evaluation metrics

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Aggregate results

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Report or store evaluation

The pipeline starts by setting criteria, then runs the model on inputs, collects outputs, evaluates them, and finally reports results.

Execution Sample

LangChain

from langchain.evaluation import EvaluationChain

# Create evaluation chain
eval_chain = EvaluationChain.from_llm(llm)

# Run evaluation
results = eval_chain.evaluate(inputs, references)

This code sets up an evaluation chain with a language model and runs it on inputs compared to references.

Execution Table

Step	Action	Input	Output	Notes
1	Define evaluation criteria	Metric: accuracy	Criteria set	Sets how outputs will be judged
2	Prepare input data	Inputs: ['Hello']	Prepared inputs	Data ready for model
3	Run model with input	Input: 'Hello'	Model output: 'Hi'	Model generates response
4	Collect model output	Model output: 'Hi'	Collected output	Output stored for eval
5	Apply evaluation metrics	Output vs Reference: 'Hi' vs 'Hello'	Score: 0.0	Calculates similarity score
6	Aggregate results	Scores: [0.0]	Aggregate score: 0.0	Combines scores if multiple
7	Report or store evaluation	Aggregate score: 0.0	Report generated	Final results ready
8	Exit	All inputs processed	Evaluation complete	Pipeline ends

💡 All inputs processed and evaluation results reported

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 5	Final
inputs	['Hello']	['Hello']	['Hello']	['Hello']	['Hello']
model_output	None	None	'Hi'	'Hi'	'Hi'
evaluation_score	None	None	None	0.0	0.0
aggregate_score	None	None	None	None	0.0

Key Moments - 3 Insights

Why do we need to prepare input data before running the model?

Preparing inputs ensures the model receives data in the correct format, as shown in Step 2 of the execution_table.

How is the evaluation score calculated?

The score compares model output to the reference using the chosen metric, demonstrated in Step 5 where output 'Hi' is compared to 'Hello'.

What happens after all inputs are processed?

The pipeline aggregates scores and reports results, ending the process as shown in Steps 6 and 7.

Visual Quiz - 3 Questions

Look at the execution_table, what is the model output at Step 3?

A 'Hi'

B 'Hello'

C 'Hey'

D None

💡 Hint

Check the 'Output' column in Step 3 of the execution_table.

At which step is the evaluation score first calculated?

A Step 4

B Step 5

C Step 2

D Step 6

💡 Hint

Look for when 'Score' appears in the 'Output' column in the execution_table.

If the input changes, which step will be affected first?

A Step 1

B Step 5

C Step 2

D Step 7

💡 Hint

Input preparation happens at Step 2 according to the execution_table.

Concept Snapshot

Automated evaluation pipelines run models on inputs,
compare outputs to references using metrics,
aggregate scores, and report results.
Steps: define criteria, prepare data, run model,
evaluate output, aggregate, then report.
This automates checking model quality.

Full Transcript

An automated evaluation pipeline in Langchain starts by defining how to judge model outputs. Then it prepares the input data so the model can understand it. Next, the model runs on these inputs and produces outputs. These outputs are collected and compared to reference answers using evaluation metrics like accuracy. The scores from these comparisons are combined into an aggregate score. Finally, the pipeline reports or stores the evaluation results. This process repeats for all inputs until complete. This helps developers check how well their models perform automatically.