Lesson 2082 of 2116
AI for Debugging Stack Traces
Use AI to interpret cryptic stack traces and locate the failing line.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2AI for Refactoring Legacy Functions
- 3The premise
- 4AI for Writing Unit Tests
Concept cluster
Terms to connect while reading
Section 1
The premise
Using AI to Debugging Stack Traces can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Interpret error chains from a clear prompt and visible context.
- Skim long traces when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 2
AI for Refactoring Legacy Functions
Section 3
The premise
Using AI to Refactoring Legacy Functions can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Rename and reshape from a clear prompt and visible context.
- Preserve semantics when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 4
AI for Writing Unit Tests
Section 5
The premise
Using AI to Writing Unit Tests can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Draft test scaffolds from a clear prompt and visible context.
- Cover edge inputs when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 6
AI for Explaining Regex Patterns
Section 7
The premise
Using AI to Explaining Regex Patterns can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Decode patterns from a clear prompt and visible context.
- Name capture groups when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 8
AI for Converting Code Between Languages
Section 9
The premise
Using AI to Converting Code Between Languages can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Translate syntax from a clear prompt and visible context.
- Flag idiom gaps when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 10
AI for Generating SQL Queries
Section 11
The premise
Using AI to Generating SQL Queries can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Draft joins from a clear prompt and visible context.
- Explain plans when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 12
AI for Reviewing Pull Requests
Section 13
The premise
Using AI to Reviewing Pull Requests can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Spot smells from a clear prompt and visible context.
- Summarize diffs when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 14
AI for Naming Variables Clearly
Section 15
The premise
Using AI to Naming Variables Clearly can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Suggest names from a clear prompt and visible context.
- Match conventions when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 16
AI for Writing Docstrings
Section 17
The premise
Using AI to Writing Docstrings can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Draft docstrings from a clear prompt and visible context.
- List parameters when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 18
AI for Mocking API Responses
Section 19
The premise
Using AI to Mocking API Responses can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Build fixtures from a clear prompt and visible context.
- Vary edge cases when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 20
AI for Reading Large Codebases
Section 21
The premise
Using AI to Reading Large Codebases can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Summarize modules from a clear prompt and visible context.
- Trace callers when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 22
AI for Writing Shell One-Liners
Section 23
The premise
Using AI to Writing Shell One-Liners can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Compose pipelines from a clear prompt and visible context.
- Explain flags when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 24
AI for Generating Project Boilerplate
Section 25
The premise
Using AI to Generating Project Boilerplate can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Scaffold configs from a clear prompt and visible context.
- Set defaults when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 26
AI for Writing Database Migration Scripts, Part 2
Section 27
The premise
Using AI to Writing Database Migration Scripts can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Draft migrations from a clear prompt and visible context.
- Pair up/down when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 28
AI for Explaining Error Messages
Section 29
The premise
Using AI to Explaining Error Messages can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Translate errors from a clear prompt and visible context.
- Suggest fixes when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 30
AI for Pair Programming Flow
Section 31
The premise
Using AI to Pair Programming Flow can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Ask back from a clear prompt and visible context.
- Challenge assumptions when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 32
AI for Converting Callbacks to Async/Await
Section 33
The premise
Using AI to Converting Callbacks to Async/Await can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Rewrite chains from a clear prompt and visible context.
- Preserve order when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 34
AI for Generating TypeScript Types
Section 35
The premise
Using AI to Generating TypeScript Types can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Infer types from a clear prompt and visible context.
- Narrow unions when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 36
AI for Writing CLI Help Text
Section 37
The premise
Using AI to Writing CLI Help Text can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Draft help from a clear prompt and visible context.
- Show examples when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 38
AI for Explaining Git Merge Conflicts
Section 39
The premise
Using AI to Explaining Git Merge Conflicts can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Summarize sides from a clear prompt and visible context.
- Suggest resolution when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 40
AI for Generating API Client Code
Section 41
The premise
Using AI to Generating API Client Code can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Scaffold clients from a clear prompt and visible context.
- Type responses when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 42
AI for Writing Integration Tests
Section 43
The premise
Using AI to Writing Integration Tests can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- List scenarios from a clear prompt and visible context.
- Stub deps when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 44
AI for Summarizing Commit History
Section 45
The premise
Using AI to Summarizing Commit History can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Draft notes from a clear prompt and visible context.
- Group themes when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 46
AI for Writing Error Handling Code
Section 47
The premise
Using AI to Writing Error Handling Code can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Place handlers from a clear prompt and visible context.
- Design retries when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Section 48
AI for Writing Configuration Schemas
Section 49
The premise
Using AI to Writing Configuration Schemas can accelerate work when you treat the model as a fast junior collaborator that needs clear inputs and human review.
What AI does well here
- Define schemas from a clear prompt and visible context.
- Validate inputs when given concrete examples.
- Produce structured drafts you can edit rather than blank-page starts.
- Surface options you might not have considered without committing to one.
What AI cannot do
- Guarantee correctness on code paths it has never seen run.
- Replace human judgment about product intent or user safety.
- Know facts about your private systems that were not in the prompt.
Key terms in this lesson
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