What OpenAI Developers Actually Build
OpenAI APIs power a wide range of applications. Understanding what developers build helps you hire effectively:
Conversational AI & Chatbots
The most common GPT application:
- Customer support bots - AI that handles customer queries naturally
- Virtual assistants - Siri/Alexa-like experiences for specific domains
- Interactive tutors - Educational AI that adapts to learners
Examples: Intercom's AI, Zendesk AI, countless startup chatbots
Content Generation Systems
Automated content at scale:
- Marketing copy - Ads, emails, product descriptions
- Technical documentation - API docs, user guides, help articles
- Creative content - Blog posts, social media, scripts
Examples: Jasper, Copy.ai, many content platforms
Code & Developer Tools
AI-powered development:
- Code completion - GitHub Copilot-style suggestions
- Code review - Automated code analysis and feedback
- Documentation generation - Auto-generate docs from code
Examples: GitHub Copilot, Cursor, Codeium
Search & Analysis
Making data accessible:
- Semantic search - Natural language queries over documents
- Data extraction - Pull structured data from unstructured text
- Summarization - Condense long documents into key points
Examples: Many enterprise search and analytics tools
Multimodal Applications
Using multiple OpenAI models:
- Image generation - DALL-E for creating visuals
- Speech-to-text - Whisper for transcription
- Vision analysis - GPT-4V for understanding images
Understanding the OpenAI Ecosystem
The Model Lineup
Know what each model does:
- GPT-4 / GPT-4 Turbo - Most capable, best for complex reasoning
- GPT-3.5 Turbo - Faster and cheaper, good for simpler tasks
- DALL-E 3 - Image generation from text prompts
- Whisper - Speech recognition and transcription
- Embeddings - Text-to-vector for search and similarity
Key Concepts for Hiring
When interviewing, these terms matter:
- Prompt engineering - Crafting inputs for optimal outputs
- Fine-tuning - Customizing models for specific use cases
- Function calling - Having GPT call your code
- Assistants API - Building persistent, stateful AI assistants
- Rate limits & quotas - Managing API constraints
- Token economics - Costs scale with usage
The Development Experience
OpenAI developers work with:
- API integration - REST APIs, SDKs (Python, Node.js)
- Streaming - Real-time token-by-token responses
- Error handling - Retries, fallbacks, graceful degradation
- Caching - Reduce costs and latency
- Monitoring - Track usage, quality, and costs
The OpenAI Developer Profile
They Think in Prompts
Strong OpenAI developers understand:
- Prompt structure - System messages, few-shot examples, formatting
- Output control - Getting consistent, structured responses
- Context management - Working within token limits
- Model selection - Choosing the right model for each task
They're Cost-Conscious
AI APIs get expensive. Good developers:
- Optimize prompts for efficiency
- Use caching strategically
- Choose appropriate models (not always GPT-4)
- Monitor and predict costs
- Implement fallbacks and degradation
They Handle Uncertainty
LLMs are non-deterministic. Strong developers:
- Build robust error handling
- Implement output validation
- Create fallback strategies
- Test with diverse inputs
- Monitor production quality
Skills Assessment by Project Type
For Chatbots & Conversational AI
- Priority: Conversation design, memory management, response quality
- Interview signal: "How do you maintain context in a long conversation?"
- Red flag: No understanding of token limits or conversation strategies
For Content Generation
- Priority: Output quality control, formatting, brand voice consistency
- Interview signal: "How do you ensure consistent quality at scale?"
- Red flag: Only knows basic completion calls, no quality framework
For Production Systems
- Priority: Reliability, error handling, cost management, monitoring
- Interview signal: "How do you handle API failures in production?"
- Red flag: No production experience, only prototype-level work
Common Hiring Mistakes
1. Thinking OpenAI Experience = Senior AI Engineer
Calling the API is accessible to anyone. Production expertise is different:
- Handling edge cases and failures
- Optimizing costs at scale
- Building reliable, maintainable systems
- Understanding when NOT to use AI
2. Over-Indexing on Specific API Knowledge
OpenAI's APIs change frequently. Focus on:
- General AI application architecture
- Problem-solving with LLMs
- API integration patterns (transferable)
- Ability to learn new APIs quickly
3. Ignoring Cost Understanding
Many developers build without cost awareness:
- Ask about cost optimization strategies
- Test understanding of token economics
- Verify they've managed real budgets
- Check they know when AI is overkill
4. Not Testing Quality Judgment
LLM outputs vary. Good developers:
- Know what "good enough" looks like
- Can evaluate output quality systematically
- Understand model limitations
- Balance quality vs. cost vs. latency
Recruiter's Cheat Sheet
Questions That Reveal Expertise
| Question | Junior Answer | Senior Answer |
|---|---|---|
| "How do you handle rate limits?" | "Retry when it fails" | Discusses exponential backoff, request queuing, multiple API keys, tier management, proactive monitoring |
| "When would you use GPT-3.5 vs GPT-4?" | "GPT-4 is better so always use it" | Explains trade-offs: cost (10x difference), latency, task complexity, when GPT-3.5 is sufficient |
| "How do you control output format?" | "Ask nicely in the prompt" | Discusses function calling, JSON mode, system prompts, validation, retry strategies |
Resume Green Flags
- Production AI applications with real users
- Cost metrics ("Reduced API costs by 60%")
- Multiple project types (chatbots AND content AND search)
- Experience with rate limits and scaling
- Mentions monitoring and evaluation
Resume Red Flags
- Only tutorial-level projects
- No production deployment experience
- Single-use-case experience only
- No mention of costs or optimization
- Claims expertise but can't explain trade-offs