Python Automation Cookbook, Part 2: Using AI to Make Decisions
Combining deterministic Python scripts with small AI prompts to handle edge cases and decision-making in automation pipelines.
Stock photo for illustration only, not from the actual event
- Use Python scripts for deterministic tasks and AI for judgment calls in pipelines.
- AI file triage prevents silent failures when processing malformed or unexpected data schemas.
- The prompt_runner.py utility handles API initialization, error handling, and batch execution.
- Reduced manual PR review triage from 20-30 minutes down to 2-3 minutes per day.
Part 2 of the Python Automation Cookbook series focuses on the challenge of chaining automation scripts together when control flow involves decision-making. While pure script-chaining works well for straightforward tasks, real-world pipelines inevitably hit decision points where data schemas change, files are empty, or responses are malformed.
Historically, developers relied on complex if/elif trees to handle these scenarios, but this approach breaks down when parsing ambiguous outputs or edge cases. The author's proven pattern combines deterministic scripts for predictable tasks like HTTP calls and file I/O, utilizing a small AI prompt specifically for judgment calls.
Stock photo for illustration only, not from the actual event
A practical implementation of this pattern involves an AI triage step that classifies raw files before conversion. Using a lightweight prompt, the AI categorizes files as PROCESS, REVIEW, or SKIP. The built-in prompt_runner.py tool manages API boilerplate, token budgeting, and automatic backoff on rate limit errors during batch execution.
Another powerful use case is automating GitHub pull request triage for codebases receiving 15-30 PRs daily. The router script categorizes incoming requests into AUTO for documentation updates and typo fixes, REVIEW for standard queue placement, and URGENT for immediate on-call notifications.
"Reduce manual triage from 20-30 minutes per day to 2-3 minutes. AUTO classifications are almost always right (>95%). URGENT classifications have had 0 false negatives in 6 months — it's conservative on purpose."
Peyton Green
Integrating AI into automation pipelines shifts the burden of maintaining endless if/elif condition trees to a flexible prompt-based classifier. The general break-even rule is simple: if writing code to handle a specific edge case takes longer than 10 minutes, making an API call is often more cost-effective and sustainable for long-term pipeline maintenance.
Source: Dev.to
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