Lesson 357 of 2116
Reproducibility: Making Your AI-Assisted Work Re-Runnable
AI-assisted research is especially vulnerable to reproducibility failures. Model versions shift, prompts drift, outputs vary. Here's how to lock it down.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1Why AI-assisted work fails reproducibility tests
- 2reproducibility
- 3seed
- 4temperature
Concept cluster
Terms to connect while reading
Section 1
Why AI-assisted work fails reproducibility tests
Traditional reproducibility says: publish your code and data, and anyone can re-run. AI adds three new failure modes: the model you used may be deprecated; the same prompt may produce different outputs next week; and stochastic sampling means even identical prompts produce different outputs across runs.
The lock-down checklist
- 1Record the exact model ID (e.g., 'gpt-4o-2024-08-06', 'claude-sonnet-4-5-20250929')
- 2Record the temperature, top-p, and max-tokens settings
- 3Set a random seed where the API supports it
- 4Version-control every prompt as a text file, not buried in a notebook
- 5Cache the raw model outputs alongside the derived results
- 6Document the date of every API call — model behavior changes silently
What to publish
- The full prompts (as .txt or .md files in your repo)
- The model IDs and settings (as a config file)
- The raw responses you actually used (as JSON)
- Any post-processing code
- A README that walks through re-running the whole pipeline
💡 Try it yourself
Open an AI assistant (Claude, ChatGPT, or Gemini) and practice what you just learned.
→ Apply the concept from this section in a real prompt
→ Note what worked and what surprised you
Key terms in this lesson
The big idea: AI-assisted work is only reproducible if you treat prompts as code and cache outputs as data. Everything else is a promise you can't keep.
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