Description

A focused course, tailored for you

The QA Lead Playbook for AI-Generated Trading Code

How a Software QA Lead at a US broker-dealer signs off releases where parts of the codebase were written by an AI agent.

Your release sign-off ticket has your name on it. Part of the diff was written by an AI coding assistant. FINRA, the SEC, and your internal supervisory team still hold you accountable for what shipped. There is no playbook for the evidence package this combination requires, and the auditors are starting to ask.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

Software QA Leads inside US broker-dealers are the last human signature before a release reaches production trading systems. The codebase increasingly contains functions, refactors, and test scaffolds generated by Copilot, Cursor, or an internal agent. The QA discipline that worked when every line had a human author does not address: which model produced which diff, what prompt sat behind it, who reviewed the AI output, what rejection rate the team is running, and how a supervisory analyst reconstructs the chain six months later when a trade is flagged. SEC Rule 17a-4 records-retention, FINRA supervisory obligations, and internal model-risk governance all converge on the QA evidence package. Most QA Leads are improvising it. This course turns the improvisation into a documented, defensible practice.

What you walk away with

Produce a documented QA evidence package that a FINRA or SEC examiner can read end to end.
Run a release sign-off process that distinguishes human-authored, AI-authored, and AI-assisted code paths.
Set a defensible AI-output rejection rate and track it across releases.
Map regression-testing patterns to the reality that the next agent run can rewrite the function under you.
Brief Engineering, Compliance, and Internal Audit on the supervisory documentation the QA function now owns.

The 12 modules

Module 1. The new QA accountability surface

Walks through what changed when AI coding assistants entered the regulated codebase: which artefacts now matter for sign-off, which records the firm must retain, and where the QA Lead sits in the supervisory chain. Uses a US broker-dealer release flow as the worked example and identifies the five specific questions a future examiner will ask about an AI-touched diff.

Module 2. SEC Rule 17a-4 and the records-retention question for AI-written code

Explains how 17a-4 applies to AI-generated source artefacts: the model identifier, the prompt, the AI output, the human review, the reject trail. Maps each to the firm's existing WORM storage and books-and-records policy. Covers the practical question of whether prompts are business records and how supervisory teams are currently answering it.

Module 3. FINRA supervisory obligations and the QA sign-off ticket

Connects FINRA Rules 3110 and 3120 supervisory frameworks to the release sign-off ticket the QA Lead signs. Covers the evidence a supervisory analyst expects to find when reconstructing a release, the escalation paths when an AI-generated function touches order-routing or client-account logic, and the language to use in the sign-off comment so the supervisory record is clean.

Module 4. Model and prompt logging for the QA evidence package

Concrete templates for logging which model produced which diff, the prompt that elicited it, the AI output before edit, and the human reviewer's accept or reject decision. Covers integration with common pipelines, the retention period implied by 17a-4, and what to do when the AI vendor changes the underlying model without notice.

Module 5. Rejection rate as a QA discipline metric

Establishes the AI-output rejection rate as a first-class QA metric alongside defect escape and coverage. Walks through how to calibrate it, how to report it to Engineering and Risk, and what a defensible rejection rate looks like for safety-critical trading code versus internal tooling. Includes a worked example of a rejection-rate dashboard.

Module 6. Regression testing when the agent can rewrite the function under you

Addresses the regression-testing problem unique to agent-assisted development: the next AI run can refactor a function the QA team already cleared. Covers immutable test contracts, regression-test pinning to specific code SHAs, snapshot strategies for trading-logic functions, and how to detect a silent behavioural change introduced by a routine refactor.

Module 7. The supervisory analytics handshake

Walks through what the supervisory analytics function inside a broker-dealer needs from QA to reconstruct a flagged trade six months after release. Covers the link from trade event to release SHA to AI-generated diff to prompt to reviewer. Provides a documented handshake spec the QA Lead can take to the supervisory team.

Module 8. AI-assisted unit and integration tests

Handles the inverse problem: tests written by an AI assistant. Covers the credibility question (does an AI-written test actually test the thing it claims), the cross-check patterns that catch tautological tests, and how to record the AI authorship of test code so the evidence package does not over-claim coverage.

Module 9. Vendor and tooling governance for AI coding assistants

Covers the vendor diligence the QA Lead should expect Procurement and Technology Risk to have done on Cursor, Copilot, Codeium, or an internal agent. Identifies the gaps the QA function typically discovers (data flow on the prompt, training data, model version pinning) and the questions to raise before they become an examination finding.

Module 10. Incident response when AI-generated code fails in production

Walks the QA Lead through the first 48 hours after an AI-generated function causes a production incident on a trading system. Covers the supervisory disclosure question, the records that must be preserved, the language for the internal post-incident review, and the regulatory notification triggers that may activate.

Module 11. Briefing Engineering, Compliance, and Internal Audit

Provides three short briefing decks the QA Lead can adapt: one for Engineering on what changed in the sign-off discipline, one for Compliance on the records the QA function now produces, one for Internal Audit on the control framework around AI-generated code. Each deck has a worked example and a Q&A appendix.

Module 12. The 90-day implementation plan for the QA function

A week-by-week plan to move the QA function from improvised AI-code sign-off to a documented practice. Names the artefacts to produce, the conversations to have with Engineering and Compliance, the metrics to publish to leadership, and the audit-ready evidence package the QA Lead presents at the end of the 90 days.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

If you are running release sign-off this Friday on a diff that contains AI-generated code, start at Module 1 and Module 3.

If Internal Audit has asked what records you keep on AI-coding assistant usage, start at Module 2 and Module 4.

If a supervisory analyst has asked how to reconstruct a flagged trade back to the code that produced it, start at Module 7.

If an AI-generated function caused a production incident, start at Module 10.

What you get with this course

Twelve written modules in the Art of Service learning environment.
Sign-off ticket templates differentiating human, AI-assisted, and AI-authored code paths.
Model and prompt logging schema aligned to SEC 17a-4 records-retention.
Rejection-rate metric calibration worksheet and dashboard mock-up.
Briefing decks for Engineering, Compliance, and Internal Audit.
90-day implementation plan with weekly checkpoints.
A hand-built implementation playbook tuned to a US broker-dealer QA function.

What you will have in hand by Day 1, Week 1, Month 1

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

Modules 1 through 4 can be worked through in the first week.

Modules 5 through 8 in the second week.

Modules 9 through 12 in the third week.

The 90-day implementation plan begins after Module 12.

Before and after

Before

Releases with AI-generated code ship under the QA Lead's signature without a documented evidence package. Rejection rate is not tracked. The supervisory team cannot reconstruct a trade from the release artefacts. Internal Audit has begun asking questions the QA function does not have written answers to.

After

Every release carries a documented evidence package distinguishing human, AI-assisted, and AI-authored code. Rejection rate is a published QA metric. The supervisory team has a handshake spec. Internal Audit has a control framework to test against. The QA Lead has a written sign-off discipline that holds up to an examination.

What happens if you do not address this

When a regulator asks the firm to demonstrate supervisory control over AI-generated code, the answer either exists in the QA evidence package or it does not. Firms whose QA function cannot produce the evidence package face supervisory findings, remediation orders, and in the worst case an enforcement referral. The QA Lead is the person whose signature is on the sign-off ticket. The records discipline is the QA function's to own, or to lose.

Who it is for

A Software Quality Assurance Lead inside a US broker-dealer or asset manager, responsible for release sign-off on systems that touch order routing, client accounts, or supervisory analytics. Likely managing a small QA team, working with developers who have adopted AI coding assistants, and reporting into either Engineering or a Technology Risk function. Comfortable with test frameworks, less comfortable with the records-retention and supervisory documentation conversation that the AI-coding shift is now forcing onto the QA seat.

Who this is NOT for. Not for QA engineers in non-regulated industries. Not for compliance officers who do not own release sign-off. Not for development leads who only consume QA output. Not for anyone whose firm explicitly prohibits AI coding assistants in the regulated codebase, because the records and supervisory questions do not yet apply.

How it arrives

Text-based course in the Art of Service learning environment, plus downloadable templates and worked examples for every module, plus the hand-built implementation playbook delivered alongside course access.

Time investment. Approximately three hours per week across three weeks for the written modules and templates, then the 90-day implementation plan worked alongside the day job.

Why $199 is the right number

Generic AI-governance courses cover policy at the firm level and do not address the QA sign-off ticket. Vendor documentation from coding-assistant providers covers product features and not the records discipline a broker-dealer needs. Internal Audit content addresses the audit perspective, not the QA function's daily practice. This course is written for the QA Lead seat specifically and covers the records, supervisory, and sign-off discipline that the seat owns.

FAQ

Does this assume my firm has approved AI coding assistants?

It assumes AI coding assistants are in use, whether under formal approval or not. The QA function still owns the sign-off discipline either way, and the course covers the conversations to have with Compliance and Technology Risk if the formal approval is still in progress.

Is this specific to one AI coding assistant?

No. The records, sign-off, and supervisory patterns work whether the team is using Copilot, Cursor, Codeium, or an internal agent. Vendor-specific examples are used to illustrate, not to prescribe.

Does this duplicate what our model-risk function does?

No. Model-risk governance addresses the firm-level model inventory and validation. The QA evidence package addresses what happens at the release sign-off ticket. The course shows how the two functions hand off cleanly.

Is the implementation playbook generic?

No. The playbook is hand-built for a US broker-dealer QA function and references the records, supervisory, and audit conversations specific to that setting.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.