# Ask Your Survey Anything: Building AI Analysis Pipelines with Airflow 3

DevFeed: [Ask Your Survey Anything: Building AI Analysis Pipelines with Airflow 3](<https://devfeed.tech/articles/ask-your-survey-anything-building-ai-analysis-pipelines-with-airflow-3-32527.md>)

Original publisher: [Read original article](<https://airflow.apache.org/blog/ai-survey-analysis-pipelines/>)

Author: Apache Airflow

Published: 2026-04-15T00:00:00Z

Content type: tutorial

Language: en

Sources: [Apache Airflow Blog](<https://devfeed.tech/sources/apache-airflow-blog.md>)

Topics: [airflow](<https://devfeed.tech/topics/airflow.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [SQL](<https://devfeed.tech/topics/sql.md>), [CSV](<https://devfeed.tech/topics/csv.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [airflow](<https://devfeed.tech/tags/airflow.md>), [apache-airflow](<https://devfeed.tech/tags/apache-airflow.md>), [automated](<https://devfeed.tech/tags/automated.md>), [community](<https://devfeed.tech/tags/community.md>), [configuration](<https://devfeed.tech/tags/configuration.md>), [csv](<https://devfeed.tech/tags/csv.md>), [executor](<https://devfeed.tech/tags/executor.md>), [llm](<https://devfeed.tech/tags/llm.md>), [pipelines](<https://devfeed.tech/tags/pipelines.md>), [providers](<https://devfeed.tech/tags/providers.md>), [scheduled](<https://devfeed.tech/tags/scheduled.md>), [sql](<https://devfeed.tech/tags/sql.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

## AI overview

A tutorial showing how to build interactive and scheduled AI analysis pipelines with Airflow 3. The pipelines use survey CSV data, generate and run SQL queries, validate schemas, and either request human approval or email results automatically.

## Source excerpt

The 2025 Airflow Community Survey collected responses from nearly 6,000 practitioners across 168 questions. You can open a spreadsheet and filter, or write SQL by hand. But what if you could just ask a question and have Airflow figure out the query, run it, and bring the result back for your approval? This post builds two pipelines that do exactly that, using the apache-airflow-providers-common-ai provider for Airflow 3. The first pipeline is interactive: a human reviews the question before it reaches the LLM and approves the result before the DAG finishes. The second is scheduled: it downloads fresh survey data, validates the schema, runs the query unattended, and emails the result. If you haven't seen the common.ai provider overview yet, start there for a tour of all the operators. This post goes deep on a concrete end-to-end example. Two Pipelines, One Example File Both DAGs live in example_llm_survey_analysis.py and share the same schema context and datasource configuration. example_llm_survey_interactive: trigger manually, review at both ends: prompt_confirmation -> generate_sql -> run_query -> extract_data -> result_confirmation (HITLEntryOperator) (LLMSQLQuery) (Analytics) (@task) (ApprovalOperator) example_llm_survey_scheduled: runs @monthly, no human in the loop: download_survey -> prepare_csv -> check_schema -> generate_sql -> run_query -> extract_data -> send_result (HttpOperator) (@task) (LLMSchema (LLMSQLQuery) (Analytics) (@task) (@task / Email) Compare) The Data The Airflow Community Survey 2025 CSV has 5,856 rows and 168 columns covering everything from Airflow version and executor type to cloud provider, company size, and AI tool usage. A few highlights from the data: 3,320 respondents identify as Data Engineers 2,032 use AWS as their primary cloud provider for Airflow 1,445 are already running Airflow 3 1,351 say they often use AI tools to write Airflow code Those last two numbers together are part of why this example exists: the people most likely to use th