The AI Record
the main success driver we saw was choosing the right related files (finding nearby files, good example files from the same project, filtering the dependencies for files that were relevant to the component, etc.), rather than getting the prompt engineering perfect.

Charles Covey-BrandtAuthor of the Airbnb engineering blog post on LLM test migration (role not stated on the page) · Practitioner, Airbnb

Post: Accelerating Large-Scale Test Migration with LLMs, Airbnb Engineering & Data Science (own site), March 13, 2025, at 3. Increasing the Context.

Checked against the source [ Grade B ] One to two years old Airbnb: $12.2 billion revenue, FY2025

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Who is speaking

Charles Covey-Brandt, Author of the Airbnb engineering blog post on LLM test migration (role not stated on the page). Wrote the post describing the migration the employer carried out, so the account is first-hand; it is also Airbnb's own account of its project.

How this was checked

Checked against the source by a first reader, then confirmed independently by a second reader who fetched the source without seeing the first reader's notes (Independent review agent (Claude Sonnet), blind second pass from passage text and citation only, no repository access, 2026-11-04).

Exact match, including the parenthetical list, in the Increasing the Context section. Author byline Charles Covey-Brandt.

How to cite this passage

Charles Covey-Brandt (Airbnb), “Accelerating Large-Scale Test Migration with LLMs”, Airbnb Engineering & Data Science (own site), 2025-03-13, at 3. Increasing the Context. https://airbnb.tech/?p=366 Archived at https://the-ai-record.com/passages/airbnb-main-success-driver-choosing-right-related-2025

More from the same source

when we ran our first bulk run, we successfully migrated 75% of our target files in just four hours.

Charles Covey-BrandtAuthor of the Airbnb engineering blog post on LLM test migration (role not stated on the page) · Practitioner, Airbnb

Post: Accelerating Large-Scale Test Migration with LLMs, Airbnb Engineering & Data Science (own site), March 13, 2025, at 3. Increasing the Context.

  • 75% first run migration share, first bulk run. No person in the loop

Checked against the source [ Grade B ] One to two years old Airbnb: $12.2 billion revenue, FY2025 Passage page

we found the most effective route to improve outcomes was simply brute force: retry steps multiple times until they passed or we reached a limit.

Charles Covey-BrandtAuthor of the Airbnb engineering blog post on LLM test migration (role not stated on the page) · Practitioner, Airbnb

Post: Accelerating Large-Scale Test Migration with LLMs, Airbnb Engineering & Data Science (own site), March 13, 2025, at 2. Retry Loops & Dynamic Prompting.

Checked against the source [ Grade B ] One to two years old Airbnb: $12.2 billion revenue, FY2025 Passage page

We'd originally estimated this would take 1.5 years of engineering time to do by hand, but — using a combination of frontier models and robust automation — we finished the entire migration in just 6 weeks.

Charles Covey-BrandtAuthor of the Airbnb engineering blog post on LLM test migration (role not stated on the page) · Practitioner, Airbnb

Post: Accelerating Large-Scale Test Migration with LLMs, Airbnb Engineering & Data Science (own site), March 13, 2025.

  • 6 weeks migration duration, Enzyme to React Testing Library migration of nearly 3.5K test files. A person approves

Checked against the source [ Grade B ] One to two years old Airbnb: $12.2 billion revenue, FY2025 Passage page

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