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Real-World AI Coding Interview Projects: From Requirements to Time-Boxed Delivery

Learn to scope, audit, design, delegate AI tasks, test, demo, and document real-world AI Coding interview projects.

About 24 min read
AI Coding interview project delivery guide

Project Overview

This repository compiles three AI Coding project assignments from real hiring processes and distills a general methodology for time-boxed coding tests, take-home assignments, and live pair-programming interviews. The assignments have been anonymized and restructured and do not include the names of any companies, interviewers, recruiters, or clients.

The repository primarily contains project retrospectives and methodology. The three corresponding personal implementations are hosted in separate repositories containing source code, design documents, tests, and actual delivery records.

What these projects truly assess is not merely the amount of code written, but also requirements comprehension, architectural boundaries, process control, acceptance evidence, and the ability to make trade-offs.

What the Three Projects Assess

01: Computer Use Agent Dashboard

The first assignment requires building a system that combines the Web, an Agent, and isolated desktops. It focuses on Computer Use, tool-calling loops, session isolation, observability, and runtime reliability.

For this type of project, simply implementing model API calls and a user interface is not enough. You must also consider how the Agent invokes tools, how different sessions are isolated, and whether failure states can be observed and recovered from.

02: React Native Screenshot Action Agent

The second assignment is a 48-hour React Native/iOS project. It uses screenshots as input and focuses on multimodal understanding, structured Actions, Human-in-the-loop workflows, native tools, and Memory.

The key to this type of assignment is creating a complete loop: understand the screenshot, generate a structured action, ask the user for confirmation, and execute it through appropriate native capabilities rather than merely outputting natural-language advice.

03: Jira-Style Task Management System

The third assignment requires completing a Web MVP in one day. It primarily assesses requirements prioritization, CRUD, Kanban drag-and-drop, local persistence, timelines, and overall delivery quality.

This project also preserves 24 real AI Coding conversation rounds spanning the PRD through the repository Wiki, offering insight into how requirements, implementation, and documentation were developed incrementally.

View ForceTrack’s 24 real conversation rounds

Core Method: Complete an AI Coding Project in Six Steps

The General AI Coding Project Playbook divides the complete process into six stages. Whether the assignment involves an Agent, a mobile app, or a CRUD system, you can follow this sequence.

Step 1: Clarify Requirements and Freeze the Scope

First, convert ambiguous descriptions into a verifiable P0 scope. Distinguish between what must be completed, what can be postponed, and what is explicitly out of scope to prevent requirements from continuously expanding during a time-boxed project.

You can begin by creating a simplified requirements matrix:

Requirement: What operation should the user be able to perform?
Acceptance: How can successful completion be demonstrated?
Priority: Is it part of P0?
Status: Not started, in progress, or verified?
Risk: What are the dependencies, permissions, failure paths, or time costs?

The point of the matrix is not its format, but ensuring that every task corresponds to clear acceptance evidence.

Step 2: Audit the Starter or Existing Repository

If the interviewer provides a starter repository, do not rewrite it immediately. First inspect the existing directory structure, dependencies, build process, data models, and reusable components. Then decide which parts to retain, extend, or replace.

  • Confirm that the project can be installed, built, and run successfully.
  • Identify the existing state management, persistence, and testing solutions.
  • Check platform, permission, and runtime constraints.
  • Record blockers to prevent the AI from continuing to generate code based on incorrect assumptions.

Step 3: Design the Data, State, Tools, and Runtime

Different assignments emphasize different boundaries. Agent projects require consideration of tool loops, session isolation, and failure recovery. Mobile projects require structured Actions, user confirmation, and native tools. Task management systems require attention to CRUD, drag-and-drop state, local persistence, and timelines.

The design should clearly define normal, loading, and failure states, as well as recovery methods. A happy path without edge-case handling is generally insufficient for a complete, deliverable workflow.

Step 4: Break AI Tasks into Vertical Slices

Do not ask the AI to generate the entire system at once. A more reliable approach is to proceed in vertical slices, with each slice containing the necessary data, logic, interface, and validation.

An AI task contract should specify at least the following:

Objective: The visible outcome to complete in this round
Scope: The modules that may be modified
Constraints: Technology stack, time, and compatibility requirements
Acceptance: Operations or checks that must pass
Prohibited items: Features that must not be expanded in this round

This approach reduces context drift and makes it easier for a human to review each iteration.

Step 5: Conduct Human Review and Automated Validation

AI can accelerate implementation, but developers remain responsible for technical judgment. After each round of changes, verify that the code meets the requirements, does not break existing behavior, and handles failure paths appropriately.

  • Run the existing tests and build process.
  • Manually verify the core user journeys.
  • Check state, permissions, persistence, and edge cases.
  • Confirm that the UI matches actual runtime behavior.
  • Record acceptance results rather than merely noting that “the code has been generated.”

Step 6: Make Time-Boxed Trade-Offs, Demo, and Deliver

When time is limited, proactively cut low-value scope and prioritize a complete P0 workflow. Final delivery should include not only source code but also build results, tests, demos, and documentation proving that the project actually runs.

A concise delivery checklist might look like this:

Is the core workflow complete?
Can the project be built and started?
Are critical failure paths handled?
Is data or state persisted as required?
Are the demo steps stable and reproducible?
Does the documentation explain the scope, trade-offs, and known limitations?

How to Get Started with This Repository

This is a repository of project retrospectives and methodology, not an application that requires installing shared dependencies and starting a unified service. Preparation therefore primarily involves reading the materials, copying the templates, and creating an execution plan for a specific assignment.

  1. Clone or download the current repository from its GitHub page.
  2. Read the three project assignments in order to understand the engineering goals and acceptance priorities for different project types.
  3. Read the general playbook and copy its requirements matrix, task contract, and delivery checklist.
  4. Select one assignment and reduce its requirements to a P0 scope that can be completed within the time limit.
  5. If you need to study implementation details, open the corresponding personal implementation repository to review its source code, design documents, and tests.

The README recommends that you read the three assignments first, followed by the general playbook. When working on an actual assignment, you do not need to copy every template in full; adapt or simplify them according to the project’s size and time limit.

Basic Practice Examples

Suppose you are practicing with the Jira-style task management system. You can limit the one-day goal to a verifiable core workflow, organizing implementation and acceptance around CRUD, Kanban drag-and-drop, local persistence, and the timeline. As you complete each component, verify both state updates and persistence results instead of waiting until every page is finished before testing everything together.

If you are practicing with the Screenshot Action Agent, organize vertical slices around screenshot input, multimodal understanding, structured Actions, user confirmation, and native tools. Retain human confirmation and validation at every stage rather than treating model output as a reliable execution result by default.

For the Computer Use Agent Dashboard, prioritize validating the tool loop, session isolation, observability, and runtime reliability. The interface is only one part of the system; whether the runtime process can be traced and how failures are handled are also part of the acceptance criteria.

Advanced Tips

Prioritize Acceptance Evidence over Feature Count

In a time-boxed project, three complete features that have been tested and demonstrated are generally more convincing than ten pages that cannot run reliably. Every P0 feature should be supported by evidence from user operations, tests, build results, or documentation.

Use AI to Accelerate Implementation, Not Replace Decision-Making

AI is well suited to tasks with clearly defined boundaries, but developers must retain control over scope, architectural trade-offs, risk assessment, and final Review. The more ambiguous the task, the greater the risk of rework when generating large amounts of code at once.

Prioritize State and Recovery Paths

Although the three assignments take different forms, all involve state, failures, recovery, permissions, persistence, or edge cases. Before the demo, proactively test invalid input, tool failures, state transitions, and behavior after reopening the application.

Use Real Conversation History to Review the Collaboration Process

ForceTrack’s 24 real conversation rounds are useful for analyzing process control in AI Coding. While reading them, focus on how each round’s objective is narrowed, how tasks are divided, and how results evolve incrementally from the PRD into the repository Wiki.

Conclusion

The value of these materials lies not only in providing three practice assignments, but also in demonstrating a reusable AI Coding delivery approach: understand the requirements and freeze the scope, audit the environment, design the boundaries, break down tasks, conduct human reviews, and finally complete acceptance through tests, builds, demos, and documentation.

The assignment details, time limits, and confidentiality requirements of actual hiring processes may differ. When applying these methods, always follow the official materials you receive and proactively remove low-value scope based on the available time.