ChatGPT and Codex
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Choose the surface according to whether the desired outcome is reasoning, a reusable artefact, or verified codebase change.
What You Will Be Able to Decide
- Explain chatgpt and codex in product and business terms.
- Apply this decision: Choose the surface according to whether the desired outcome is reasoning, a reusable artefact, or verified codebase change.
- Recognise this material risk: a persuasive conversation is mistaken for repository-aware implementation evidence.
- Use this review: Run one customer email through the intake tool with missing context, ambiguous intent, and a request that needs human approval.
A founder is deciding what to delegate to AI and what evidence to require before accepting the result. This lesson gives you a concrete question to take into a build brief, proposal review, or product decision.
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? The course example is An AI assisted intake tool that turns customer emails into draft tasks; use it to decide what evidence would justify the choice before a builder implements it.
What Does ChatGPT and Codex Mean for Your Product?
A founder is deciding what to delegate to AI and what evidence to require before accepting the result.
Use the illustrative service for this course (An AI assisted intake tool that turns customer emails into draft tasks) to make the choice concrete. What may the AI produce, what must it never decide, and what evidence lets a person approve the result?
Technical term
ChatGPT and Codex
ChatGPT is a general conversational workspace, while Codex is oriented towards carrying out software work against a repository and development environment.
How Should a Founder Use ChatGPT and Codex?
For an ai assisted intake tool that turns customer emails into draft tasks, ask what would happen if a persuasive conversation is mistaken for repository-aware implementation evidence.
For this decision, the useful standard is that the output satisfies explicit constraints and survives review outside the conversation that produced it.
- Decision: Choose the surface according to whether the desired outcome is reasoning, a reusable artefact, or verified codebase change.
- Evidence to request: show that the output satisfies explicit constraints and survives review outside the conversation that produced it.
- Owner: name who will respond if a persuasive conversation is mistaken for repository-aware implementation evidence.
- Record the result in the AI work brief, review record, and acceptance criteria.
- Practical review: Run one customer email through the intake tool with missing context, ambiguous intent, and a request that needs human approval.
How Do You Choose an Approach to ChatGPT and Codex?
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Choose the surface according to whether the desired outcome is reasoning, a reusable artefact, or verified codebase change.
The risk is that a persuasive conversation is mistaken for repository-aware implementation evidence. Compare a simpler option with the proposed one, including who will operate either choice.
- Describe the user or business outcome that must be protected.
- Identify the most credible failure and its consequence.
- Compare the simplest adequate approach with one realistic alternative.
- Set a review point for when the decision may need to change.
What Evidence Should You Accept for ChatGPT and Codex?
What Warning Signs Should You Look For?
- The proposal does not address this risk: a persuasive conversation is mistaken for repository-aware implementation evidence.
- Nobody can show whether the output satisfies explicit constraints and survives review outside the conversation that produced it.
- The decision has no named owner or review point.
What Should You Ask a Consultant?
- What changes for the user if we choose this approach to chatgpt and codex?
- How have we reduced or accepted this risk: a persuasive conversation is mistaken for repository-aware implementation evidence.
- Can you demonstrate that the output satisfies explicit constraints and survives review outside the conversation that produced it?
- Who owns the result, and when will we reconsider it?
Key takeaway
Key Takeaway
Choose the surface according to whether the desired outcome is reasoning, a reusable artefact, or verified codebase change. Ask for evidence against the specific risk: a persuasive conversation is mistaken for repository-aware implementation evidence.