AIT-PY-01
Python workflow implementation
Uses core Python constructs to build readable, modular automation for an AI-related task.
Loading your workspace…
Loading your workspace…
AI & TECHNOLOGY · COMPLETE DRAFT · READY FOR REVIEW
An applied Python course for reading data, calling model services, validating structured results, testing behavior, and producing a reviewable AI evaluation utility.
GOVERNANCE GATE
This version exposes the proposed sources, competencies, instruction, evidence requirements, and rubric for accountable review. It cannot be enrolled in, completed, or used to issue a credential until an authorized reviewer approves a final immutable version and the program is separately published.
Status: Complete draft awaiting accountable instructional review
Draft version: 1.0.0-draft.1
Estimated learning time: 240 minutes
Program level: Course
Evidence activities: 3
Credential pathway: Proposed Certificate of Completion after review, publication, learner evidence approval, and separate administrator issuance.
LEARN → APPLY → DEMONSTRATE → REVIEW → VERIFY
AIT-PY-01
Uses core Python constructs to build readable, modular automation for an AI-related task.
AIT-PY-02
Validates inputs and outputs, handles files and JSON, protects secrets, and responds to API errors.
AIT-PY-03
Creates repeatable tests, records outputs, diagnoses failures, and documents run and revision evidence.
CONTINUING PROFESSIONAL CASE
A team evaluates model responses by copying prompts and scores into spreadsheets. Results are inconsistent, secrets appear in notebooks, and failed requests are silently omitted.
Constraint: The utility must run from the command line, use a small JSONL dataset, protect credentials, validate outputs, and produce repeatable evidence without requiring a web framework.
Learner task: Build and document a Python utility that loads cases, calls or simulates a model adapter, scores structured results, handles failures, and writes a summary.
FIVE SOURCE-GROUNDED LESSONS
LESSON 1
Purpose: Represent cases and process them explicitly.
Python's strings, numbers, booleans, lists, and dictionaries provide the core representation for prompts, expected properties, results, and metrics. Control flow should make missing and invalid states visible.
Professional example: A loop validates each case ID and prompt before evaluation.
Evidence connection: The Python Tutorial defines language data structures and control-flow behavior through executable examples.
Represent one evaluation case as a dictionary and validate required fields before processing.
What is the best response to a missing required key?
Expected: Detect it explicitly and record or raise a clear error. — Validation makes invalid state visible.
LESSON 2
Purpose: Separate responsibilities into testable units.
Functions should accept clear inputs, return clear outputs, and avoid hidden dependencies. Modules separate loading, model access, scoring, and reporting so each part can be tested.
Professional example: score_case(case, response) returns a structured result rather than printing from deep inside the function.
Evidence connection: The Python Tutorial documents function definitions, parameters, return values, modules, and coding style.
Define interfaces for load_cases, generate_response, score_case, and write_report.
Which function is easiest to test?
Expected: One with explicit inputs and a returned result. — Explicit interfaces support isolated testing.
LESSON 3
Purpose: Handle external data without confusing code and configuration.
Programs should validate file paths and encodings, parse JSON deliberately, keep secrets in approved environment or secret stores, and avoid logging sensitive values.
Professional example: The utility reads JSONL cases, reports line-specific parse errors, and loads an API key without printing it.
Evidence connection: Python's standard library documents file and JSON behavior; API guidance uses environment-based credentials.
Implement the dataset loader and configuration boundary for the evaluation utility.
Where should an API key be stored?
Expected: In an approved secret or environment configuration excluded from logs and version control. — Credentials should remain outside source and ordinary outputs.
LESSON 4
Purpose: Treat remote responses as fallible external input.
An API adapter should set timeouts, handle authentication and rate errors, validate response shape, distinguish retryable failures, and preserve enough evidence for diagnosis without duplicating unsafe effects.
Professional example: A failed case records error type and attempt count instead of disappearing from the summary.
Evidence connection: Current API quickstarts show authenticated client requests; robust applications must add validation and explicit error handling.
Design the model adapter's timeout, retry, validation, and error-result behavior.
What should happen after a malformed response?
Expected: Record a validation failure and apply the defined retry or stop policy. — External responses require validation and bounded failure handling.
LESSON 5
Purpose: Demonstrate behavior beyond a successful demo.
Tests should cover normal cases, invalid data, adapter failures, scoring boundaries, and report completeness. Fixed fixtures and recorded run instructions let another person reproduce the evidence.
Professional example: A stub adapter returns known responses so scoring and reporting tests do not require network access.
Evidence connection: Python supports assertions, exception testing, modules, and deterministic fixtures through its language and standard library ecosystem.
Create tests for valid input, malformed JSON, missing fields, timeout, scoring boundary, and complete summary counts.
Which test evidence is strongest?
Expected: Repeatable tests cover success and failure with documented versions and commands. — Reproducible tests make technical capability inspectable.
ASSESSED APPLICATION
PROPOSED REVIEW RUBRIC
Problem framing and operational boundary is explicit, workable, and supported by relevant evidence.
Competency: AIT-PY-01
Technical design and implementation rationale is explicit, workable, and supported by relevant evidence.
Competency: AIT-PY-02
Evaluation design and preserved evidence is explicit, workable, and supported by relevant evidence.
Competency: AIT-PY-03
Risk controls and accountable governance is explicit, workable, and supported by relevant evidence.
Competency: AIT-PY-03
Failure analysis, revision, and professional judgment is explicit, workable, and supported by relevant evidence.
Competency: AIT-PY-03
PRIMARY SOURCE BASE
Python Software Foundation · Python 3 Documentation · 3.14.7 documentation accessed August 2026
Provides the primary language tutorial for data structures, control flow, functions, modules, files, exceptions, and classes.
Python Software Foundation · Python Standard Library · 3.14 documentation accessed August 2026
Provides primary documentation for safe JSON serialization and parsing used in data and API workflows.
OpenAI · OpenAI API Documentation · Current documentation accessed August 2026
Provides a current primary example of authenticated Python model requests and response handling.