Point it at an Airflow DAG and get idiomatic Prefect code back. Raw source in, working flow out. Thirty seconds, not thirty hours.
# 1. Read the DAG source code
dag = read_dag("dags/etl_pipeline.py")
# 2. Look up Airflow→Prefect translation knowledge
mapping = lookup_concept("PythonOperator")
docs = search_prefect_docs("task retries")
# 3. LLM generates idiomatic Prefect code
# 4. Validate the generated flow
result = validate(
original_dag=dag_path,
converted_flow=prefect_code
)
# ✓ Syntax valid
# ✓ Both sources returned for comparisonTraditional conversion tools rely on rigid templates that break on the first edge case. We give the LLM raw source code and translation knowledge. It writes the Prefect code.
• Rigid pattern matching breaks on variations
• Non-idiomatic output requires manual fixes
• Cannot handle custom operators well
• Template maintenance burden
• Source code + translation knowledge in, working code out
• Idiomatic Prefect, not templates with TODOs
• Handles custom operators and edge cases
• Gets better as models get better