From Confusion to Trust: My In-Depth Experience with Codex

Codex is redefining the standards of AI programming tools. With its delivery integrity, reliable debugging capabilities, and stability in complex tasks, it has become a core productivity tool for developers. This article delves into Codex’s four core technical advantages, revealing how it addresses long task stability issues and how product managers assess product success through unique metrics.

Why Codex is My Favorite AI Product

The main reasons I chose Codex are its delivery integrity, usability, reliable debugging capabilities, and the stability and security it provides for complex tasks.

Currently, Codex is the AI product I use most frequently and for the longest duration daily, deeply embedded in my workflow, completing over 70% of my product execution tasks.

Core Advantages in Experience

1. High Quality of One-Time Delivery: When requirements are clear, Codex delivers a complete and usable result in one go.

2. Strong Problem Diagnosis Ability: After providing accurate feedback, issues can typically be resolved in 1-2 interactions, rarely causing new problems.

3. Stable Instruction Compliance: It excels in complex long tasks, delivering stable and reliable results.

4. Safe and Controllable: With a strong self-management awareness, it rarely performs actions beyond user control, fostering a sense of trust (to the point that I now somewhat overtrust it).

Relative Weaknesses Compared to Other AI Programming Tools

1. Poor at Proactive Divergence: While it excels at executing development under clear requirements, it struggles with proactive exploration and creative expansion.

2. Average Performance on Non-Programming Tasks: Its performance on creative writing and text analysis tasks is inferior to Claude Code, likely due to differing product positioning, as Claude Code clearly aims toward general intelligence.

3. Weak Front-End Design Capabilities: The front-end pages generated by GPT-5.4 have a single style, favoring card layouts, and its visual design capabilities are noticeably inferior to Gemini and Claude. It is speculated that OpenAI may have used a relatively outdated front-end database as training data.

4. Not User-Friendly for Non-Technical Users: Its technical dialogue style poses a certain barrier for novices, although there have been noticeable improvements recently.

My Current Use of Codex

Currently, I complete 70% of my execution tasks in Codex, covering the following five work scenarios:

1. Requirement information organization and assistance in requirement research
2. Writing requirement documents and other project-related documents
3. Data cleaning and visualization analysis
4. Product prototype development
5. Technical solution exploration and validation

Workflow Setup

Typically, I create a separate folder for each product or project, within which I set up two common tools:

– Prompts: Used for completing regular product tasks

– Skills: Used for handling special tasks and validating technical solution MVPs

Prompt Templates (4 Types)

1. Requirement analysis and organization
2. Requirement document writing
3. Requirement review
4. Prototype generation and inspection

The above scenarios use prompts instead of being directly solidified into Skills, as adjustments are often needed based on specific project requirements.

Skill Usage Scenarios

Skills are created based on project needs, mainly for two situations:

1. Complex Data or Specialized Task Handling: When dealing with data processing or other complex work, corresponding Skills are created based on actual conditions. For example, if the requirement involves processing a large number of legal documents, I create a specific Skill for “Extracting Rules” to quickly complete the preliminary processing of the regulations.

2. Technical Feasibility Validation: In AI-related requirements, Skills can also be used to quickly validate feasibility. For such requests, I habitually run a complete workflow using Skills before handing it over to technical personnel for in-depth iteration and implementation, significantly reducing validation costs.

In-Depth Analysis of Codex’s Agent Loop

After using it for a while, I have been curious about how Codex operates and why it remains stable in long tasks. This article from OpenAI helped me understand several key product designs:

1. Flexible Model Inference and Prompt Construction

Codex initiates requests through a configurable Responses API, supporting not only OpenAI-hosted models and the ChatGPT backend but also seamless integration with cloud service providers like Azure or locally running open-source models (such as through Ollama or LM Studio). In prompt construction, users do not need to manually assemble underlying prompts; Codex automatically converts queries into a JSON payload containing instructions, tools, and input. Before sending requests, it injects sandbox permissions, working directory environments, local shell states, and multi-level developer instruction files into the context.

2. Performance Optimization Based on Prompt Caching

In the agent loop, the accumulation of dialogue history leads to a quadratic increase in the data sent to the API. Since the cost of model sampling far exceeds that of network transmission, Codex heavily relies on prompt caching to enhance efficiency. By ensuring that old prompts are precise prefixes of new ones, the sampling overhead can drop from quadratic complexity to linear complexity.

3. Context Compression to Avoid Resource Exhaustion

Each large language model has a fixed context window, and a complex task may involve hundreds of tool calls, risking token exhaustion. Codex’s strategy is dialogue compression: when the token count exceeds a threshold, it automatically calls the /responses/compact endpoint to replace extensive historical records with a concise project summary list, freeing up context space.

4. Zero Data Retention for Enterprise Data Security

Codex actively abandons the previous_response_id parameter to ensure all API requests are completely stateless. This design directly supports zero data retention (ZDR) configuration, greatly simplifying the onboarding process for enterprise clients with high data security standards.

Metrics Product Managers Focus on for Codex

To deeply understand a product, one often cannot overlook the people behind it. Codex’s product manager, Alexander Embiricos, left a strong impression on me. In interviews he participated in, he revealed many product details and shared his views on product development in the AI era, demonstrating a thoughtful approach to the industry’s evolution.

He mentioned an enlightening point:

“If you are only good at building websites but lack a clear customer base, you will struggle; however, if you deeply understand the unmet needs of a specific group, you can succeed.”

He believes that building products with AI has become very easy, and the true competitive edge for entrepreneurs is shifting towards a deep understanding of specific customer problems. This is also reflected in the specific metrics he values:

1. D7 Retention Rate and Early Retention Data

Developing tools like Codex can easily fall into the trap of over-designing “advanced user features,” focusing solely on the geek demographic. However, since the AI programming field is still in its early stages of user adoption, he places great importance on the seventh-day retention rate and early retention performance of new users, as those who remain are truly convinced by the product.

2. Task Parallelism and High-Frequency Usage Data

He assesses whether users have reached their “aha moment” by observing the number and patterns of tasks initiated by users. Specifically, if users can run up to 20 tasks in parallel within a day with a “growth mindset,” it indicates they truly understand how to use the tool.

3. First-Person Experience of New Users

He often simulates the complete process of new users using a newly registered Gmail account, even accumulating some personal subscription bills in the process. He wants to see firsthand what a person encountering the product for the first time will experience.

4. Social Media Sentiment and Real User Feedback

He and his team frequently monitor community discussions on Twitter and Reddit, paying particular attention to user complaints and criticisms, as negative feedback often pinpoint specific failure points in the product under certain scenarios more accurately than praise.

As a product manager, it is easy to think about “focusing on user retention,” but considering “parallel task count” as a proxy metric for determining whether users have truly engaged is a perspective I had not previously considered.

The moment users begin to “let loose” with the tool is when they truly trust the product, which mirrors my own journey from “not understanding” to “overtrusting” Codex. Reflecting on my experience with Codex, from initial confusion to deep trust, illustrates the process of adapting to a tool and a shift in mindset. Alexander’s mention of a “growth mindset” not only serves as a metric for user depth but also embodies the essence of our survival in this AI era: when we no longer get bogged down by the underlying implementation details but can trust and drive agents to solve complex problems in parallel, our human judgment and deep insights into needs truly realize their value.

In this AI-driven “Age of Exploration,” understanding the customers you wish to serve and having the courage and execution to evolve alongside agents in this unpredictable environment may be the most critical factors.