Lesson 10 of 12

Structured learning draft

Evaluation with LangChain

In Building AI Agents & LLM Apps, the way a learner handles evaluation shapes how LangChain is used and evaluated. Evaluation compares behaviour on representative tasks using explicit criteria. This advanced lesson focuses on a decision or output that another person can inspect.

Learning objectives

Explain evaluation in the context of Building AI Agents & LLM Apps.
Apply LangChain to a bounded practical task.
Evaluate the result using explicit quality criteria.

Instructional figureSide-by-side comparison

Evaluation: from context to evidence

Evaluation connects bounded input to a reviewed output in Building AI Agents & LLM Apps.

Bounded input

Define the purpose, intended user and LangChain constraints.

frames

Evaluation

Build a test set with pass conditions, edge cases and failure categories.

produces evidence for

Reviewed output

Compare the observed result with a normal case, boundary case and stated limitation.

Takeaway: Evaluation is credible only when the result can be traced back to its purpose, inputs and constraints. Evaluation is the decision layer between the starting context and evidence that the result is fit for purpose.

Evaluation compares behaviour on representative tasks using explicit criteria. For LangChain, distinguish performing an operation from demonstrating that it suits the stated purpose. Build a test set with pass conditions, edge cases and failure categories. Record assumptions that could change the conclusion.

Apply evaluation deliberately

State the Building AI Agents & LLM Apps task and the decision it supports.
Prepare a small LangChain case with a known input and difficult boundary.
Build a test set with pass conditions, edge cases and failure categories.
Compare the observed result with the expected behaviour and explain differences.
Save the evidence, limitation and next action in a review record.

evaluation evidence record
Review point	Evidence
Purpose	The specific LangChain outcome and intended user
Method	The evaluation decision, input and version or context
Result	Observed output plus a checked boundary case
Limitation	What the result does not establish and the next safe action

Common mistakes

Using LangChain before defining what evaluation must achieve.
Checking only the easiest Building AI Agents & LLM Apps example.
Reporting a result without its input, assumptions or limitation.

Practice activity

Apply the lesson

For Building AI Agents & LLM Apps, complete a bounded LangChain task demonstrating evaluation. Keep the original input, numbered method, normal test, boundary test, observed results and a 100-word self-review naming one limitation and next improvement.

Check your understanding

In Building AI Agents & LLM Apps, which evidence best supports a evaluation result produced with LangChain?

A polished LangChain output without its input The input, expected behaviour, normal and boundary results, and a limitation A screenshot showing the tool opened A claim that the method always works

Lesson summary

For Building AI Agents & LLM Apps, evaluation means: Evaluation compares behaviour on representative tasks using explicit criteria.
A credible LangChain result includes a checked boundary, not only a successful example.
The next lesson builds on this evaluation evidence record.

Sources and further reading

AI Risk Management Framework 1.0NIST - accessed 2026-08-21
AI PrinciplesOECD - accessed 2026-08-21

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