Training in Prompt Engineering

Learn about how training happens in prompt engineering.

We'll cover the following

Copilots
- GitHub copilot
Codex
- Our AI engine learning path

Our thoughts are often chaotic. Humans use many methods to reconstruct unstructured sentences. Humans often need to ask additional questions to understand what somebody is talking about. We need to accept this when interacting with a trained transformer such as OpenAI GPT-3.

Keep in mind that a $\text{dialogue d(T)}$ with a transformer and the response, $\text{maxR(response)}$ , depends on the quality of our inputs, $\text{minI(prompt)}$ , as defined previously:

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Course Introduction

Basics of Transformers

Architecture of the Transformer Model

Fine-Tuning BERT Models

Pretraining a RoBERTa Model from Scratch

Downstream NLP Tasks with Transformers

Machine Translation with the Transformer

The Rise of Transformers with GPT-3 Engines

Applying Transformers for AI Text Summarization

Matching Tokenizers and Datasets

Transformers for NLP—Exam 1

Semantic Role Labeling with BERT-Based Transformers

Your Data Speaks: Story, Questions, and Answers

Detecting Customer Emotions to Make Predictions

Analyzing Fake News with Transformers

Interpreting Black Box Transformer Models

From NLP to Task-Agnostic Transfomer Models

The Emergence of Transformer-Driven Copilots

Transformers for NLP—Exam 2

Conclusion

Appendix

Training in Prompt Engineering