use-cases/learn-a-new-concept.md +266 −0 added
1# Learn a new concept | Codex use cases
2
3Codex use cases
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5
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7
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9Codex use case
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11# Learn a new concept
12
13Turn dense source material into a clear, reviewable learning report.
14
15Difficulty **Intermediate**
16
17Time horizon **30m**
18
19Use Codex to study material such as research papers or courses, split the reading across subagents, gather context, and produce a Markdown report with diagrams.
20
21## Best for
22
23 - Individuals learning about an unfamiliar concept
24- Dense source material that benefits from parallel reading, context gathering, diagrams, and a written synthesis
25- Turning a one-off reading session into a reusable Markdown report with citations, glossary terms
26
27# Contents
28
29[← All use cases](https://developers.openai.com/codex/use-cases)
30
31Copy page [Export as PDF](https://developers.openai.com/codex/use-cases/learn-a-new-concept/?export=pdf)
32
33Use Codex to study material such as research papers or courses, split the reading across subagents, gather context, and produce a Markdown report with diagrams.
34
35Intermediate
36
3730m
38
39Related links
40
41[Subagents](https://developers.openai.com/codex/subagents) [Subagent concepts](https://developers.openai.com/codex/concepts/subagents)
42
43## Best for
44
45 - Individuals learning about an unfamiliar concept
46- Dense source material that benefits from parallel reading, context gathering, diagrams, and a written synthesis
47- Turning a one-off reading session into a reusable Markdown report with citations, glossary terms
48
49## Skills & Plugins
50
51- [ImageGen](https://github.com/openai/skills/tree/main/skills/.curated/imagegen)
52
53 Generate illustrative, non-exact visual assets when a Markdown-native diagram is not enough.
54
55| Skill | Why use it |
56| --- | --- |
57| [ImageGen](https://github.com/openai/skills/tree/main/skills/.curated/imagegen) | Generate illustrative, non-exact visual assets when a Markdown-native diagram is not enough. |
58
59## Starter prompt
60
61 I want to learn a new concept from this research paper: [paper path or URL].
62 Please run this as a subagent workflow:
63- Spawn one subagent to map the paper's problem statement, contribution, method, experiments, and limitations.
64- Spawn one subagent to gather prerequisite context and explain the background terms I need.
65- Spawn one subagent to inspect the figures, tables, notation, and any claims that need careful verification.
66- Wait for all subagents, reconcile disagreements, and avoid overclaiming beyond the source material.
67 Final output:
68 - create `notes/[concept-name]-report.md`
69- include an executive summary, glossary, paper walkthrough, concept map, method diagram, evidence table, caveats, and open questions
70 - use Markdown-native Mermaid diagrams where diagrams help
71- use imagegen to generate illustrative, non-exact visual assets when a Markdown-native diagram is not enough
72 - cite paper sections, pages, figures, or tables whenever possible
73 Constraints:
74 - do not treat the paper as ground truth if the evidence is weak
75 - separate what the paper claims from your interpretation
76 - call out missing background, assumptions, and follow-up reading
77
78 I want to learn a new concept from this research paper: [paper path or URL].
79 Please run this as a subagent workflow:
80- Spawn one subagent to map the paper's problem statement, contribution, method, experiments, and limitations.
81- Spawn one subagent to gather prerequisite context and explain the background terms I need.
82- Spawn one subagent to inspect the figures, tables, notation, and any claims that need careful verification.
83- Wait for all subagents, reconcile disagreements, and avoid overclaiming beyond the source material.
84 Final output:
85 - create `notes/[concept-name]-report.md`
86- include an executive summary, glossary, paper walkthrough, concept map, method diagram, evidence table, caveats, and open questions
87 - use Markdown-native Mermaid diagrams where diagrams help
88- use imagegen to generate illustrative, non-exact visual assets when a Markdown-native diagram is not enough
89 - cite paper sections, pages, figures, or tables whenever possible
90 Constraints:
91 - do not treat the paper as ground truth if the evidence is weak
92 - separate what the paper claims from your interpretation
93 - call out missing background, assumptions, and follow-up reading
94
95## Introduction
96
97Learning a new concept from a dense paper or course requires more than just summarization. The goal is to build a working mental model: what problem it addresses, what the method actually does, which evidence supports it, what assumptions it depends on, and which parts you still need to investigate.
98
99Codex is useful here because it can automate the context gathering, and can turn complicated concepts into helpful diagrams or illustrations. This use case is also a good fit for [subagents](https://developers.openai.com/codex/concepts/subagents): one thread can read the paper for structure, another can gather prerequisite context, another can inspect figures and notation, and the main thread can reconcile the results into a report you can review later.
100
101For this use case, the final artifact should be something you can easily review: a Markdown file such as `notes/concept-report.md`, or a document of another format. It should include a summary, glossary, walkthrough, diagrams, evidence table, limitations, and open questions instead of ending with a transient chat answer.
102
103## Define the learning goal
104
105Start by naming the concept and the output you want. A narrow question makes the report more useful than a broad summary.
106
107For example:
108
109> I want to understand the main idea in this research paper, how the method works, why the experiments support or do not support the claim, and what I should read next.
110
111That scope gives Codex a concrete job. It should teach you the concept, but it should also preserve uncertainty, cite where claims came from, and separate the paper's claims from its own interpretation.
112
113## Running example: research paper analysis
114
115Suppose you want to learn about a paper about an unfamiliar model architecture. You want a report that lets you understand the concept at a glance, without having to read the whole paper.
116
117A good result might look like this:
118
119- `notes/paper-report.md` with the main explanation.
120- `notes/figures/method-flow.mmd` or an inline Mermaid diagram for the method.
121- `notes/figures/concept-map.mmd` or a small SVG that shows how the prerequisite ideas relate.
122- An evidence table that maps claims to paper sections, pages, figures, or tables.
123- A list of follow-up readings and unresolved questions.
124
125The point is to make the learning process more systematic and to leave behind a durable artifact.
126
127## Split the work across subagents
128
129Subagents work best when each one has a bounded job and a clear return format. Ask Codex to spawn them explicitly; Codex does not need to use subagents for every reading task, but parallel exploration helps when the paper is long or conceptually dense.
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131For a research paper, a practical split is:
132
133- **Paper map:** Extract the problem statement, contribution, method, experiments, limitations, and claimed results.
134- **Prerequisite context:** Explain background terms, related concepts, and any prior work the paper assumes.
135- **Notation and figures:** Walk through equations, algorithms, diagrams, figures, and tables.
136- **Skeptical reviewer:** Check whether the evidence supports the claims, list caveats, and identify missing baselines or unclear assumptions.
137
138The main agent should wait for those subagents, compare their answers, and resolve contradictions. Codex will then synthesize the results into a coherent report.
139
140## Gather additional context deliberately
141
142When the paper assumes background you do not have, ask Codex to gather context from approved sources. That might mean local notes, a bibliography folder, linked papers, web search if enabled, or a connected knowledge base.
143
144If you're learning about an internal concept, you can connect multiple sources with [plugins](https://developers.openai.com/codex/plugins) to create a knowledge base.
145
146Keep this step bounded. Tell Codex what counts as a reliable source and what the final report should do with external context:
147
148- Define prerequisite terms in a glossary.
149- Add a short "background you need first" section.
150- Link follow-up readings separately from the paper's own claims.
151- Mark claims that come from outside the paper.
152
153## Generate diagrams for the report
154
155Diagrams are often the fastest way to check whether you really understand a concept. For a Markdown report, ask Codex for diagrams that stay close to the source material and are easy to revise.
156
157Good defaults include:
158
159- A concept map that shows prerequisite ideas and how they connect.
160- A method flow diagram that traces inputs, transformations, model components, and outputs.
161- An experiment map that connects datasets, metrics, baselines, and reported claims.
162- A limitations diagram that separates assumptions, failure modes, and open questions.
163
164For Markdown-first reports, ask for Mermaid when the destination supports it, or a small checked-in SVG/PNG asset when it does not. Ask Codex to use imagegen only when you need an illustrative, non-exact visual or something that doesn’t fit in a Markdown-native diagram.
165
166## Write the Markdown report
167
168Ask Codex to make the report self-contained enough that you can return to it later. A useful structure is:
169
1701. Executive summary.
1712. What to know before reading.
1723. Key terms and notation.
1734. Paper walkthrough.
1745. Method diagram.
1756. Evidence table.
1767. What the paper does not prove.
1778. Open questions and follow-up reading.
178
179The report should include source references wherever possible. For a PDF, ask for page, section, figure, or table references. If Codex cannot extract exact page references, it should say that and use section or heading references instead.
180
181## Use the report as a study loop
182
183The first report is a starting point. After reading it, ask follow-up questions and have Codex revise the artifact.
184
185Useful follow-ups include:
186
187- Which part of this method should I understand first?
188- What is the simplest toy example that demonstrates the core idea?
189- Which figure is doing the most work in the paper's argument?
190- Which claim is weakest or least supported?
191- What should I read next if I want to implement this?
192
193When the concept requires experimentation, ask Codex to add a small notebook or script that recreates a toy version of the idea. Keep that scratch work linked from the Markdown report so the explanation and the experiment stay together.
194
195Example prompt:
196
197Generate a script that reproduces a simple example from this paper.
198The script should be self-contained and runnable with minimal dependencies.
199There should be a clear output I can review, such as a csv, plot, or other artifact.
200If there are code examples in the paper, use them as reference to write the script.
201
202## Skills to consider
203
204Use skills only when they match the artifact you want:
205
206- `$jupyter-notebook` for toy examples, charts, or lightweight reproductions that should be runnable.
207- `$imagegen` for illustrative visual assets that do not need to be exact technical diagrams.
208- `$slides` when you want to turn the report into a presentation after the learning pass is done.
209
210For most paper-analysis reports, Markdown-native diagrams or simple SVG files are better defaults than a generated bitmap. They are easier to diff, review, and update when your understanding changes.
211
212## Suggested prompts
213
214**Create the Report Outline First**
215
216Before writing the full report, inspect [paper path] and propose the report outline.
217Include:
218- the core concept the paper is trying to explain
219- which sections or figures are most important
220- which background terms need definitions
221- which diagrams would help
222- which subagent tasks you would spawn before drafting
223Stop after the outline and wait for confirmation before creating files.
224
225**Build Diagrams for the Concept**
226
227Read `notes/[concept-name]-report.md` and add diagrams that make the concept easier to understand.
228Use Markdown-native Mermaid diagrams when possible. If the report destination cannot render Mermaid, create small checked-in SVG files instead and link them from the report.
229Add:
230- one concept map for prerequisites and related ideas
231- one method flow diagram for inputs, transformations, and outputs
232- one evidence map connecting claims to paper figures, tables, or sections
233Keep the diagrams faithful to the report. Do not add unverified claims.
234
235**Turn the Report Into a Study Plan**
236
237Use `notes/[concept-name]-report.md` to create a study plan for the next two reading sessions.
238Include:
239- what I should understand first
240- which paper sections to reread
241- which equations, figures, or tables need extra attention
242- one toy example or notebook idea if experimentation would help
243- follow-up readings and questions to resolve
244Update the report with a short "Next study loop" section.
245
246## Related use cases
247
248[
249
250### Coordinate new-hire onboarding
251
252Use Codex to gather approved new-hire context, stage tracker updates, draft team-by-team...
253
254Integrations Data](https://developers.openai.com/codex/use-cases/new-hire-onboarding)[
255
256### Generate slide decks
257
258Use Codex to update existing presentations or build new decks by editing slides directly...
259
260Data Integrations](https://developers.openai.com/codex/use-cases/generate-slide-decks)[
261
262### Analyze datasets and ship reports
263
264Use Codex to clean data, join sources, explore hypotheses, model results, and package the...
265
266Data Analysis](https://developers.openai.com/codex/use-cases/datasets-and-reports)