Category Archives: OpenAI

There are (at least) four ways to generate JSON formatted output when using the OpenAI responses.create() method: 1.) supply detailed instructions and examples, 2.) craft a JSON schema by hand, 3.) craft a JSON schema using inference from an example, … Continue reading →

Bottom line: At the time I’m writing this blog post, I could find no official documentation for the responses.parse() method. As best I can determine, responses.create() is a general purpose method that can do many things (including produce output in … Continue reading →

I’m slowly but surely wading through examples of the OpenAI Responses API. Some people can learn just by reading, but I can only learn by coding. Briefly, to have a conversation, you need to remember the responses of the previous … Continue reading →

If you ask the OpenAI system a question using the programmatic API, it will answer to the best of its ability using the information that the underlying LLM (GPT-4o, etc.) was trained on. But if you ask about recent events, … Continue reading →

I was working on a problem using the OpenAI system and I needed the output to be in JSON format rather than the usual free-form text. Producing JSON output can be accomplished by feeding a JSON schema to the responses.create() … Continue reading →

The OpenAI Responses API can search supplied files in a vector store (this is called retrieval-augmented generation, RAG), or search the Web. I tried to combine the two tools into a single query but it just didn’t work: . . … Continue reading →

I was investigating retrieval-augmented generation (RAG) systems using the OpenAI Responses API. In a nutshell, a RAG system allows a query to use information from specific proprietary documents, such as a company handbook. Until recently, to implement RAG, you’d have … Continue reading →

A plain vanilla OpenAI natural language AI application (ChatPGT) can answer general questions about topics such as the ones you can find on Wikipedia, because the underlying LLM model was trained using many sources, including Wikipedia. But in order to … Continue reading →

I was working on a natural language processing (NLP) project. For reasons that would take to long to explain, I needed to tokenize text so that spaces, punctuation, and contractions are retained. For example, suppose some source text is: Howdy. … Continue reading →