Overview

Natural Language Processing (NLP) is often* considered to be the combination of two branches of study - Natural Language Understanding (NLU) and Natural Language Generation (NLG). *For example, that is how Ines Montani, co-founder of spaCy recently described the fields in a podcast interview. Large Language Models can do both NLU and NLG. In this course we are primarily interested in the NLU aspect - more specifically we are interested in how to extract structured information from free form text. (There is also an NLG aspect to the course which you will notice as you watch the video lessons).Recently both GPT and Gemini introduced the ability to extract structured output from the prompt text. As of this writing (November 2024), they are the only LLMs which provide native support for this feature via their API itself - in other words, you can simply specify the response schema as a Python class, and the LLMs will give you a "best effort" response which is guaranteed to follow the schema. It is best effort because while the response is guaranteed to follow the schema, sometimes the fields are empty. This course provides a practical and systematic approach for assessing the accuracy of LLM Structured Output responses. Who should take this course?Everyone who is interested in extracting structured information using LLMs can benefit from taking this course. And specifically, you should take this course if you have asked yourself any of the following questions after GPT and Gemini APIs started supporting Structured Outputs:1 Does specifying the schema as a Python class actually work? (Yes, but there are some limitations)2 Is one of the APIs better than the other? (Probably)3 Can you design your Python class in a way which provides more accurate responses? (you might be surprised to find the answer to this)4 Does this mean prompt engineering is not necessary anymore? (It is becoming less important for this particular use case)Biomedical NLPNote: I do NOT have a background in biomedicine, but I have now spent over two years on a volunteer project which requires the use of practical NLP tools to extract information from a popular biomedical dataset and have done so quite successfully.As a student, you do not need to have a background in biomedicine either to complete this course, but it does help if you have an interest in the subject.

Skills

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