Resources and workflow to learn prompt engineering

I have been thinking of how to learn prompt engineering. At one level, prompt engineering is a natural and intuitive task. But as the domain matures, we are seeing more awareness of the need for a formal process. The key point is to develop a creative/prompt engineering mindset. This is not a technical role but rather a creative (and iterative) process. This makes prompt engineering different to traditional technical skills we are familiar with such as MLOps. With this background, I list a set of resources and strategies to learn prompt engineering for technical people.

Prompt engineering mindset

I believe that the mindset is key. There are a number of strategies for developing a prompt engineering mindset aka working with the machine to get the best possible outcomes. Some of these include:

“Describe like it already exists”

“Provide Instructions and Guidance, not a Formula.”

“Think and Describe in Analogies.”

“Genius in a Room.”

Source: Four Tips for Developing A Prompt Engineering Mindset

Foundations

Large Language Models

LLM technology foundations – ex transformers

LLM model choices

Modalities(code, images and language)

Components of LLMs

LLM architectures

Prompt workflow

where do we start

Overall workflow

How are LLMs trained?

Prompting and Fine-Tuning

Prompting strategies

Role Prompting

Few shot prompting

Combining Techniques

Chain of Thought Prompting

Zero Shot Chain of Thought

LLM applications

Legal AI Prompts, Startup AI Prompts, Sales AI Prompts, Content Writing AI Prompts , E-Commerce AI Prompts, Education AI Prompts, Customer Service AI Prompts, Human Resources AI Prompts, Product Management AI Prompts, Development AI Prompts , Design AI Prompts , Marketing AI Prompts, Finance AI Prompts

Managing data and fine tuning

Preprocessing

Input Representations:

Word embeddings:

Prompt Engineering Tools

Prompt Engineering IDEs

Challenges and limitations

Future of LLMs

References

Semantic kernel

https://github.com/microsoft/semantic-kernel/tree/main/samples/apps/copilot-chat-app

LLM glossary