Practical OpenAI API Lessons for Developers

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We’ve spent countless hours refining how to best leverage OpenAI’s API. We’ve learned what works, what doesn’t, and how to avoid costly mistakes. This guide is specifically for those working with OpenAI APIs—not the app or browser-based interactions.

1. You Can’t Just Throw PDFs at OpenAI

A common mistake is assuming OpenAI can directly process a PDF. It can’t. You need to extract text first, and that process requires some key considerations:

• Convert PDFs to text before sending data to OpenAI.

• Store a Base64 version alongside the original for easier processing.

• Extract sections separately to optimize AI processing.

For example, if you’re analyzing post-doctoral dissertations, you might extract and store key sections—title page, footnotes, citations, and full text—so AI doesn’t have to parse the entire document every time. If you only need metadata, just send the extracted title, author, and date. This reduces processing time, API costs, and improves accuracy.

2. API URLs and Models Are Not the Same Thing

It may seem obvious, but many developers confuse API URLs with AI models. Think of it this way:

• API URLs are how you communicate with OpenAI.

• Models are the brains that process your request.

Choosing the Right API

Different tasks require different APIs:

• For one-time text generation or structured data extraction: Use the completions API.

• For editing and refining existing text: The edits API is the right tool.

• For multi-turn chat workflows: The Assistants API is built for that purpose.

Using the wrong API won’t just waste money—it simply won’t work as expected.

3. Use the Right Model (Or Risk Burning Cash)

OpenAI provides multiple models, each optimized for different use cases. Some are built for speed, others for creativity or logic-heavy tasks. Choosing the wrong model can be expensive and inefficient.

• Don’t overspend: gpt-3.5-turbo ($0.0002 per token) is often sufficient; gpt-4 ($0.004 per token) should be reserved for complex tasks.

• Match models to tasks: Use logic-driven models for structured work and creative models where necessary.

• Mix and match: You can use different models for different stages in a workflow.

Example: Efficient Model Use

When processing financial reports, you can:

1. Use a cheaper, faster model to extract key data.

2. Apply a more advanced model to analyze insights.

This structured approach improves speed and cost efficiency.

4. One API Call, Multiple Roles

AI doesn’t have to be limited to a single input-output interaction. Within a single API call, you can assign multiple roles to handle different tasks.

For example, when processing SL-3 Publications for the USMC, we split tasks into specialized roles:

• Extractor: Pulls structured information from the text.

• Confidence Estimator: Evaluates how accurate the extraction is.

• Verifier: Cross-checks and refines confidence levels.

• Justifier: Provides reasoning for assigned confidence scores.

This structured approach improves quality control and transparency for human review.

5. One Process Can Require Multiple API Calls

Integrating OpenAI effectively requires breaking down processes into multiple steps. One API call is rarely enough to complete an entire workflow.

For example, when we process an SL-3 Publication, we:

• Make at least 8 API requests.

• Create 5 different tables for structuring the extracted data.

• Process, validate, and transform responses before final integration.

Best Practices for Multi-Step AI Workflows

• Build on the front end first: Debugging in real-time prevents issues before backend automation.

• Separate OpenAI API responses from your data pipeline: Store raw responses and refine them through structured ETL (Extract, Transform, Load) workflows.

• Iterate before scaling: Moving to the backend prematurely makes debugging difficult and costly.

This method allows for testing, validation, and optimization before committing to full backend automation.

Why We’re Sharing This

If you’re integrating OpenAI into your applications, we want you to succeed. These insights come from real-world engineering experience, not theory.

If you have questions, need guidance, or just want to brainstorm, feel free to reach out. And if you want us to build something for you—we’re happy to help.