Essentials
8 guided hoursModules 1-2. An introduction with practical exercises and a small reviewed artifact.
AI TOOLS · SOME CODING FAMILIARITY
Build with AI. Understand every change.
Learn a practical workflow for coding with Codex: give clear requirements, provide project context, inspect changes, run checks and iterate towards a working result.
COURSE OVERVIEW
Practise briefing an AI coding assistant, providing project context, inspecting changes, debugging and running appropriate checks. The final project is a small tool whose code and verification results you can explain.
PROMPTING IN EVERY MODULE
Each of the six modules includes a sample prompt and a specific quality check. Compare revisions and keep a record of what improves the result.
Explore the Prompting FoundationA STARTING POINT THAT FITS
Modules 1-2. An introduction with practical exercises and a small reviewed artifact.
Modules 1-6, including Essentials. Complete the core labs and an assessed practical project.
Two extension modules and a four-hour project clinic after practitioner readiness.
These are curriculum planning options. We confirm the timetable, delivery format, fees and required accounts before enrolment. Practitioner includes Essentials; the two durations are not added together.
Download the Curriculum PDFWHAT YOU’LL LEARN
Students, aspiring developers and existing developers who want to work more effectively with an AI coding assistant.
Basic familiarity with code, a terminal and files. Python automation is a useful starting point if you are new to programming. Codex access is required for exercises.
THE COURSE OUTLINE
Each module connects a concept to a practical task. Open a module to see its topics and exercise.
Codex works more usefully when the learner can identify entry points, data and tests. Inspect a small project manually before asking for changes. Riverstone Learning's starter reads fictional course records from JSON and prints titles. A learner should run it independently and explain the result, rather than treating generated setup instructions as proof. Record the starter's behaviour and test results before modifying it. Version control shows which lines changed and provides a recoverable starting point. Initialise Git only in the practice folder and keep fictional files separate from workplace repositories. This makes the later diff a meaningful comparison instead of a mixture of unrelated work. Choose a supported Codex environment available to the class, but do not assume account access or installation. Review requested commands and network use. The starter needs only Python's standard library and can run offline. Without Codex access, students can execute a supplied proposed patch manually and practise the same inspection and testing steps.
Run a starter project and document its setup commands.
Inspect this practice project and explain how to run it. Identify setup requirements from its files. Do not edit code or use real credentials.
Run the documented setup yourself and check whether the explanation matches the repository.
A feature brief needs inputs, expected outputs and constraints that a learner can test. 'Improve search' is ambiguous. 'Match titles case-insensitively and return all courses for a blank query' specifies meaningful cases. Describe Riverstone Learning's intended user behaviour before requesting code, so the implementation follows an agreed contract. One bounded feature is easier to review than a full application rewrite. Name the relevant file and preserve existing behaviour unless the brief changes it. Avoid mixing search, styling and deployment in the same exercise. A small diff allows learners to explain why each edit exists and identify accidental scope expansion. A meaningful test fails when a required behaviour is wrong. Include normal, empty and no-match inputs, with exact expected results. A test that merely checks a list exists does not prove search works. Prewrite expectations before the implementation so learners do not redefine correctness to match whatever Codex generated.
Write a brief for one small feature before asking for changes.
Add the scoped feature described below. Follow the existing conventions, identify the relevant files and satisfy these acceptance criteria. Ask if an essential requirement is missing.
Inspect the diff and check each acceptance criterion rather than accepting a completion claim.
An AGENTS.md file can supply repository conventions and verification expectations in supported Codex workflows. Keep it concise and applicable to the practice project. State the standard-library constraint, fixture-only data and unittest command. Instructions help the agent work consistently, but the learner still checks whether the actual change follows them. Provide the specific files, acceptance rules and examples needed for a task rather than a large unrelated dump. Riverstone Learning's simple course tool does not need customer records or production tokens. Clear context improves review because another learner can follow the same inputs and understand why the proposed code belongs in the project. A comment or fixture string may contain requests that do not belong to the user's brief. Treat that content as data unless it is an applicable project instruction from an authorised source. Do not let a generated suggestion introduce package installation, network access or secret printing. Explicit boundaries support inspection; they do not replace judgement.
Add concise project instructions and compare the next result.
Use these project instructions and sample files to propose the smallest relevant change. Respect the stated constraints and keep secrets out of source and output.
Check that the proposed change follows the project instructions and introduces no unrelated edits.
Review a feature by following data from caller to result, not only by reading a change summary. Identify how each field is used and which behaviour remains unchanged. For Riverstone Learning's fee summary, inspect the handling of zero values and optional level filtering. A line-by-line explanation should connect edits to acceptance criteria. Compute known answers before running generated code. If a test repeats the same implementation logic, both may share a bug. Use fixed fixture totals and clearly chosen edge cases. Riverstone Learning's three course fees can be added by hand, so students have an independent reference that does not depend on Codex's reported calculation. A focused function plus meaningful tests is easier to evaluate than an unnecessary architectural rewrite. Review whether existing APIs, command output or data changed without a requirement. Keep additions proportionate to the small project. This helps learners see scope as a quality criterion while still making sufficient changes to satisfy the feature.
Build a small feature and review its changes line by line.
Implement this small feature, then explain the changed files and run the relevant checks. Include a clear report of failures and unresolved behaviour.
Review the actual code and verification results, including an edge case the initial brief did not cover.
A useful bug report states the command or function call, input, expected result and actual result. Reproduce the failure before modifying code. Riverstone Learning's case-insensitive search bug is visible with uppercase input, while lowercase input still passes. This contrast helps distinguish a specific defect from a general project setup problem. Add a test that fails against the buggy implementation and passes after the correction. An unrelated passing test does not establish a fix. Show the before-and-after result, then rerun the whole suite. This gives students concrete evidence that the changed line addresses the reported behaviour without breaking earlier features. A plausible fix can mask symptoms while introducing unnecessary edits. Ask Codex to identify the cause from code and reproduction evidence, then make the smallest adequate change. Inspect whether the query and title are normalised consistently. Keep environment failures separate from application bugs so the report describes what actually happened.
Fix a deliberately introduced bug and verify it stays fixed.
Reproduce this bug from the steps below, identify its cause and make a focused fix. Add or run a meaningful check that would catch the failure again.
Re-run the reproduction and inspect the check; a passing unrelated test is not sufficient evidence.
A useful README explains setup, commands, function contracts and limitations from the working project. It should not advertise capabilities that were merely discussed. Riverstone Learning's course tool uses a local fictional JSON fixture; it does not submit enrolments or process payments. Learners verify every instruction by following it from a clean practice copy. List the checks actually run and their results, including unavailable commands or unresolved failures. Do not confuse a static review with execution. Explain what the test suite covers and what it omits. This lets another person assess the evidence and choose further checks without relying on a general claim that everything works. Compare the final diff with the original brief and remove accidental changes. Confirm fictional data, no added credentials and no unintended network behaviour. Explain material limitations, such as exact level matching or fixed fixture values. A concise handover connects the changed behaviour to practical use and remaining review needs.
Demonstrate the project and explain your verification evidence.
Review this final change against the original brief. Run the project’s required checks and write setup, usage and remaining limitations for the handover.
Compare the handover with the working project and independently confirm the reported checks.
AFTER THE CORE COURSE
Validated totals with failure cases
Final independent-review clinic
Complete both four-hour extension modules and a four-hour project clinic. Entry uses the core project and readiness evidence.
BRING THE SKILLS TOGETHER
Use Codex to build a focused task tracker or file-reporting tool. Keep the scope small enough to understand the implementation, test it and explain the final result yourself.
A learning project using sample or permitted material. The final scope is agreed for your learning plan.
KEEP EXPLORING
Reference material to complement the Hawa Enterprises curriculum. Account features may vary; basic-feature or manual alternatives support the core exercises.
Course outline published by Hawa Enterprises. Updated . Ask about the learning plan.
GOOD TO KNOW
You will get more from this course if you can read basic code and use a terminal. Start with Automate with Python or ask us to discuss a suitable foundation first.
No. This is an independent Hawa Enterprises course outline. It does not imply OpenAI affiliation, certification or endorsement.
These details are confirmed by email before you commit. The curriculum gives guided-hour options for Essentials, Practitioner and Advanced. Actual schedules, fees and delivery arrangements are confirmed before enrolment. Ask about the course you want and your preferred timeline.
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