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PATHWAY PROJECT · STARTER PROJECT · 2 SMALL IDEAS + A GUIDED LAB

School-podcast credit builder

A neat credit line is not useful if it invents a creator or omits the source.

You will learn to: Build exact credits from supplied records, route missing or disallowed material to review, and distinguish attribution from permission.

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IDEA 1

Attribution preserves who made what

Attribution identifies the creator and the material being used. In this fictional podcast, each asset record supplies a title, creator name, and source label. Keeping those fields exact matters: rewriting a name to sound more familiar can credit the wrong person, even if the resulting sentence looks polished.

The program's job is formatting and validation, not inventing metadata. If a record is missing or a required field is blank, route the asset to review. This is a useful tool workflow for an agent system because the controller can obtain stable records before generating prose, rather than asking a model to recall ownership from memory.

Record to credit line

  1. Asset ID. Read the selected fixture record.
  2. Required fields. Preserve title, creator, and source label exactly.
  3. Format. Join only the supplied fields.
  4. Review. Keep incomplete material out of the completed credits.

All creators and assets here are invented. Source labels identify fixture records, not external links.

An exact identity

const creator = "Maya Chen";
// Do not rewrite it as "M. Chang" or infer a missing name.

Identity fields are evidence, not creative-writing prompts. The program should copy them faithfully from the designated record.

Think it through: The creator field is blank. What should the credit builder do?

IDEA 2

Credit and permission answer different questions

A credit line says who made an asset. Permission determines whether it may be used under the applicable terms and context. Giving credit does not automatically grant permission. This exercise has a simplified allowedForFixture flag authored for each invented asset; it does not interpret real licenses or provide legal advice.

Check the use flag before formatting a finished credit. If the flag is false, mark the asset not_allowed; if the record is absent, mark missing_record. These outcomes help a student choose another fixture asset or ask a reviewer. The program never downloads real media, clones a voice, or publishes the podcast.

Two different checks

  1. Record exists. Find the asset before making any attribution claim.
  2. Use flag. Respect the authored exercise permission.
  3. Credit complete. Only then format the required identity fields.

The fixture permission flag is a teaching simplification, not a real licensing decision.

Credit does not change the flag

{ creator: "Maya Chen", allowedForFixture: false }
// A known creator does not turn false into permission.

Attribution and permission are independent requirements. Satisfying one does not satisfy the other.

Think it through: An asset has a complete credit but its fixture use flag is false. Which output fits?

Put it into practice

Format exact supplied metadata while producing specific review reasons for unusable records.

  1. Run the incomplete-record case and inspect the starter's unfinished credit.
  2. Insert the required-field guard.
  3. Run every case and compare the three review reasons.
  4. Try an invented missing ID in custom input; do not fill it with guessed metadata.

Your next experiment: Add a second asset with a missing sourceLabel. Which credit can be completed, and which needs review?

Preserve exact attribution and check permission separately; missing metadata is a review task, not a writing prompt.

Key terms

Attribution
Identifying the creator and source of material.
Metadata
Structured information describing an item, such as its title and creator.
Provenance
The record of where material or a claim came from.

Sources and scope

Original Stemtiq teaching, reviewed 2026-09-14. The named researchers and organizations do not endorse this course. Classroom cases are authored exercises, not published findings.

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Lewis et al. · NeurIPS: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

NeurIPS 2020

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Your JavaScript really runs. The model decisions and school data are authored simulations, so you can learn without an API key. Every workspace also includes a separate real SDK example to explore next. Passing the lab’s cases is practice, not proof that an agent is ready for real-world use.