AI / Core OpenAI Codex Application Fundamentals Interview Questions
1. What is OpenAI Codex and how has it evolved from its original form?
OpenAI Codex began as a language model specifically trained for code generation, first released in 2021 as the engine behind GitHub Copilot. Over time, "Codex" evolved into something broader: OpenAI's agentic software engineering platform - a cloud-based coding agent that...
2. What are the current recommended models for OpenAI Codex and the API, and when do you choose each?
Model selection in the OpenAI / Codex ecosystem as of mid-2026 centres on the GPT-5.x family. The right choice depends on task complexity, latency requirements, and cost....
3. What is the OpenAI Responses API and how does it differ from the Chat Completions API?
The Responses API ( /v1/responses ), launched in March 2025, is OpenAI's recommended API primitive for new projects. It is a superset of the Chat Completions API, providing everything Chat Completions offers plus built-in agentic capabilities....
4. What are the built-in tools available in the OpenAI Responses API?
The Responses API ships with several built-in tools that the model can invoke automatically without you writing wrapper code. These tools connect the model to the real world and the developer's environment....
5. What is the OpenAI Agents SDK and what are its four core primitives?
The OpenAI Agents SDK (launched March 2025, evolved from the experimental Swarm project) is an open-source, lightweight framework for building multi-step agentic workflows on top of the Responses API and other providers. It is available for Python ( openai-agents )...
6. What is the Codex CLI and what are its key features?
The Codex CLI is an open-source, terminal-native agentic coding tool that brings OpenAI's coding models directly into your command line. It was rebuilt in 2025 around agentic workflows, making it significantly more capable than a simple code-generation prompt wrapper....
7. What is the Chat Completions API and when should you still use it?
The Chat Completions API ( /v1/chat/completions ) is OpenAI's original and most widely adopted API, introduced with GPT-3.5 and GPT-4. It uses an array of messages with role (system, user, assistant) and content fields to generate responses....
8. What is function calling (tool use) in the OpenAI API and how does it work?
Function calling (referred to as tool use in the Responses API) allows you to describe external functions to the model in a structured JSON schema format. The model then decides when and how to call those functions, returning structured arguments...
9. What are structured outputs in the OpenAI API and how do you use them?
Structured outputs guarantee that a model's response strictly conforms to a developer-defined JSON schema. This eliminates the need for output parsing heuristics and makes AI outputs reliably machine-readable....
10. What is the OpenAI Assistants API and what is its deprecation timeline?
The Assistants API was OpenAI's original high-level framework for building stateful, multi-turn AI assistants with persistent threads, file handling, and built-in tools. It introduced key concepts like Threads (conversation state), Runs (execution instances), and Vector Stores (document retrieval
11. What is prompt caching in the OpenAI API and how does it reduce costs?
Prompt caching allows OpenAI's servers to reuse portions of a prompt that were computed in a previous request, reducing both latency and cost. When a significant portion of your prompt matches a cached prefix, you are charged a reduced rate...
12. What are reasoning models in the OpenAI API and what is the 'effort' parameter?
Reasoning models (originating with the o1/o3 family and now integrated into the GPT-5.x line) spend additional compute "thinking" through a problem before producing a final answer. This hidden chain-of-thought reasoning dramatically improves performance on complex multi-step tasks like mathematic
13. What are OpenAI's key API authentication and security concepts?
Proper authentication and security practices are fundamental to building production OpenAI applications. Mistakes here can lead to credential exposure, unexpected costs, or data breaches....
14. What are rate limits in the OpenAI API and how do you handle them in production?
OpenAI enforces rate limits to ensure fair access and prevent abuse. Limits are applied on three dimensions and vary by model and usage tier....
15. What is the Batch API and when should you use it?
The Batch API allows you to submit many API requests asynchronously in a single file, receive results up to 24 hours later, and pay approximately 50% less than standard synchronous pricing. It is designed for large-scale, non-time-sensitive workloads....
16. What is streaming in the OpenAI API and how do you implement it?
Streaming delivers the model's output token by token as it is generated, rather than waiting for the complete response. This dramatically improves perceived performance in interactive applications by showing text appearing in real time....
17. What is OpenAI's Codex Skills feature and what are Automations?
Two higher-level Codex product features extend beyond direct coding assistance: Skills are reusable, project-specific capabilities that Codex learns and applies consistently. They go beyond writing code to encompass code understanding, prototyping, and documentation - aligned with your team's spe
18. What is Model Context Protocol (MCP) and how does it integrate with OpenAI tools?
Model Context Protocol (MCP) is an open standard for connecting AI models to external tools and data sources through a standardised interface. OpenAI has adopted MCP as a first-class integration in both the Responses API and the Codex CLI, enabling...
19. What is fine-tuning in the OpenAI API and when should you use it?
Fine-tuning creates a customised version of an OpenAI model trained on your own examples. It is used when prompting or few-shot examples are insufficient to achieve the desired style, format, or domain-specific accuracy....
20. What are embeddings in the OpenAI API and what are they used for?
Embeddings convert text (or other content) into dense numerical vectors that capture semantic meaning. Texts with similar meaning produce similar vectors, enabling mathematical operations on language: search, clustering, classification, and anomaly detection without requiring labelled training da
21. What is the OpenAI moderation API and why is it important for application safety?
The Moderation API classifies text (and now images) against OpenAI's usage policies, detecting harmful content across multiple categories. It is free to use and essential for any application that accepts user-generated content....
22. What are OpenAI's image generation models and how do you use them in applications?
OpenAI offers image generation models accessible via the Images API. As of mid-2026 the recommended models are gpt-image-2 and gpt-image-1 , following the deprecation of DALL-E 2 and DALL-E 3 in May 2026....
23. What is the OpenAI Realtime API and what use cases does it enable?
The Realtime API (generally available since 2025) enables low-latency, bidirectional audio and text streaming - powering live voice agents, speech-to-speech applications, and real-time transcription. As of 2026, the product line has expanded significantly.
24. What is retrieval-augmented generation (RAG) and how do you implement it with OpenAI?
Retrieval-Augmented Generation (RAG) is a technique that enhances a language model's responses by providing relevant context retrieved from an external knowledge base at query time. Instead of relying solely on the model's training data, RAG retrieves up-to-date or private information...
25. What is OpenAI's approach to responsible use and safety in the API?
OpenAI's usage policies, safety systems, and model training all work together to define what the API will and won't do. Understanding these boundaries is essential for building compliant, safe applications....
26. What is the OpenAI token system and how do you count and optimise token usage?
OpenAI models process text as tokens - chunks of characters roughly 3-4 characters long for English text, or about 75% of a word. Pricing is per token (input + output), so understanding tokenisation directly impacts application costs....
27. What are guardrails in the context of OpenAI application development?
Guardrails are safety and quality validation layers that intercept, evaluate, and potentially modify or block inputs and outputs at various points in an LLM application pipeline. They are especially critical in agentic systems where the model may take actions with...
28. What is the OpenAI Files API and how is it used for document management?
The Files API allows you to upload files to OpenAI's servers for use across multiple API features - fine-tuning, batch processing, assistants, and the file_search tool. Files are identified by a file ID and persist until explicitly deleted....
29. How do you implement multi-agent systems using the OpenAI Agents SDK?
Multi-agent systems decompose complex tasks across multiple specialised agents that collaborate via handoffs. Each agent focuses on what it does best, improving overall quality and maintainability compared to a monolithic agent trying to do everything....
30. What is the OpenAI Evals framework and why is evaluation critical for production applications?
Evals (evaluations) are automated tests that measure an LLM application's quality, accuracy, and reliability. OpenAI provides both an Evals API (for running evaluations programmatically) and the OpenAI Evals Framework (open-source, run locally).
31. How do you implement error handling in OpenAI API applications?
Robust error handling is essential for production OpenAI applications. The Python SDK raises typed exceptions that map to HTTP error codes, allowing fine-grained recovery strategies per error type....
32. What is the OpenAI Codex App and what are its main features for software teams?
The Codex App is the primary desktop and web interface for the full Codex product suite. It serves as a command center for agentic coding sessions, allowing individual developers and teams to delegate complex software tasks to AI agents....
33. What are the key differences between OpenAI's o-series reasoning models and the GPT series?
The o-series (o1, o3, o4) were OpenAI's dedicated reasoning models - designed to spend significant compute on hidden chain-of-thought before answering. The GPT-5.x series (mid-2025 onwards) has progressively integrated reasoning capabilities, creating a unified model line that adapts reasoning de
34. How does the Codex IDE extension integrate with development environments?
The Codex IDE Extension brings the full Codex agentic capability directly into code editors, enabling developers to access AI assistance without leaving their development environment. It is available for VS Code and supports other IDEs through the Language Server Protocol....
35. What are the key considerations for building production-grade OpenAI applications?
Moving from a prototype to a production OpenAI application requires addressing several concerns that do not arise during development: reliability, cost control, observability, and safety. Production readiness checklist Area Key considerations Cost control Set spending limits; use Batch API for...
36. What is the OpenAI Codex and API pricing model and how do you estimate costs?
OpenAI uses a pay-per-token pricing model for API access. For Codex CLI and App, costs are consumed from your ChatGPT or API credits balance....
37. What is the OpenAI computer use capability and what does it enable?
Computer use is a capability that allows an OpenAI model to interact with a computer's graphical interface - clicking buttons, filling forms, navigating browsers, and reading screen content - just as a human user would. It is available as a...
38. How do you use the OpenAI API for code generation, review, and debugging tasks?
Code-related tasks are among the most common and well-supported use cases in the OpenAI API. The following patterns apply across code generation, review, and debugging....
39. What is the role of system prompts (instructions) in OpenAI applications and how do you design them effectively?
The system prompt (called instructions in the Responses API, system in Chat Completions) sets the model's persona, behaviour, constraints, and context for the entire conversation. It is the primary lever for customising model behaviour without fine-tuning....
40. How do you handle context window management in long-running OpenAI applications?
Every OpenAI model has a finite context window - the maximum tokens (input + output) it can process in one call. In long-running applications (chat sessions, agentic workflows, document analysis), managing this window efficiently is critical for both functionality and...
