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Building with Language Models: Inputs, Outputs, and Evaluation

Understand the parts of a small language-model feature: inputs, structured outputs, evaluation, and operational limits.

10 bite-sized modules Learn at your own pace

Proposed sample course · Proposed sample price: $49 USD

COURSE CONTENT

Start with a preview. Published lessons follow approval.

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FREE PREVIEW · INTRODUCTION

A model call is one part of a feature; the surrounding validation and evaluation determine how safely it can be used.

SAMPLE COURSE OUTLINE

Outline topics only. Owner-approved learner lessons are not published yet.

01
Define the model input and output contract
Specify the input, response shape, and failure cases before connecting a model.
02
Validate generated structured data
Check model output against a schema before another part of the application uses it.
03
Handle timeouts, retries, and errors
Set practical limits and give the feature a clear path when a model call fails.
04
Build a representative evaluation set
Include expected, ambiguous, and invalid inputs with clear success criteria.
05
Compare results against explicit criteria
Review output quality consistently as prompts, inputs, or models change.
06
Set context and input-size limits
Choose useful bounds for content length and explain what happens when they are reached.
07
Plan for time and cost limits
Set reasonable caps for latency, retries, and usage in the surrounding feature.
08
Protect sensitive input data
Send only the information needed for the task and respect the tool’s data settings.
09
Design a safe human fallback
Keep a person responsible when the output is missing, invalid, or uncertain.
10
Document and monitor feature limits
Record operational assumptions and watch the reliability signals that matter.

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