[Lex Computer & Tech Group/LCTG] Prompt Engineering

[Harry Forsdick]

Folks,

Remember when Conor O'Mahony gave his three excellent talks about AI / ML
systems?  [see
https://docs.google.com/document/d/1fj8FcfD_e-NEi20O3AL_8fg45DdB136fvyCNl7V2gaM/edit?usp=sharing
]

One of the topics he talked about was "prompt engineering".

I interpreted what was said about prompt engineering to refer to the kinds
of things we all did with plain old Google to get it to return the kind of
results we were after.

I recently asked https://perplexity.ai the question "*What does "prompt
engineering" mean in the context of AI search systems?*".  I learned that
my interpretation was only partially correct.  Instead of being just things
an end-user does, prompt engineering is also something that a developer of
an AI question/answering system must do to take the query submitted by
end-users, and add additional controls to get the AI answer engine to
return a reasonable, readable answer.

One of the references in this response,
https://mitsloanedtech.mit.edu/ai/basics/effective-prompts/, discusses what
an end user should learn to do.  It basically says that this is like
learning how to ask questions of an expert who knows a lot about the
subject of your query: you can steer the answer to the one you are after if
you just ask the question correctly.  To quote from the above reference:

Prompts are your input into the AI system to obtain specific results. In
other words, prompts are conversation starters: what and how you tell
something to the AI for it to respond in a way that generates useful
responses for you. After that, you can build a continuing prompt, and the
AI will produce another response accordingly. It’s like having a
conversation with another person, only in this case the conversation is
text-based, and your interlocutor is AI.


Below is the answer I got back from Perplexity.  If you want to dig deeper
than the Perplexity answer, there are references to source material used in
the response.

Regards,

-- Harry


*What does "prompt engineering" mean in the context of AI search systems?*
Prompt engineering is a crucial process in the context of AI search
systems, particularly for large language models (LLMs) and generative AI
tools. It involves crafting well-structured and effective input queries or
instructions to guide AI models in producing desired outputs or
responses[1].

*Definition and Purpose*
Prompt engineering refers to the art and science of designing and
optimizing prompts to elicit specific behaviors from AI models[4]. Its
primary goal is to bridge the gap between human intention and machine
understanding, enabling AI systems to generate accurate, relevant, and
contextually appropriate responses[2].

*Components of Effective Prompts*

1. *Instruction*: Prompts often require specific and clear requests to
receive the desired output[3].

2. *Context*: Providing relevant background information helps the AI model
understand the task better[3].

3. *Input Data*: This includes any necessary information for the AI to
process and generate a response[3].

4. *Output Format*: Specifying the desired format of the response can
improve the accuracy and relevance of the AI's output[3].

*Role of Prompt Engineers*
AI prompt engineers serve as intermediaries between machine learning models
and human users[3]. Their responsibilities include:

1. Developing sets of inputs to train models for optimal outputs
2. Writing text-based prompts for various tasks (e.g., essay writing, blog
post generation)
3. Evaluating AI systems for idiosyncrasies
4. Training and fine-tuning emerging AI tools

*What does "prompt engineering" mean in the context of AI search systems?*
Prompt engineering plays a vital role in AI search systems by:

1. *Improving Accuracy*: Well-crafted prompts lead to more precise and
relevant responses from AI models[4].
2. *Enhancing User Experience*: Clear and concise prompts make it easier
for users to interact effectively with AI models[4].
3. *Mitigating Biases*: Careful prompt design helps minimize the risk of
generating inappropriate or biased content[4].
4. *Ensuring Consistency*: Proper prompting techniques enable more
predictable and reliable AI responses[4].

*Skills Required for Prompt Engineering*
To excel in prompt engineering, professionals should possess:

1. Understanding of AI, ML, and Natural Language Processing (NLP)
2. Programming skills, particularly in Python
3. Strong communication skills
4. Knowledge of cognitive psychology and linguistics
5. Experience with pre-trained AI models like GPT-3 or GPT-4[3]

In conclusion, prompt engineering is a critical aspect of AI search
systems, focusing on optimizing the interaction between humans and AI
models to produce high-quality, relevant, and accurate responses.

Citations:
[1] https://en.wikipedia.org/wiki/Prompt_engineering
[2] https://www.datastax.com/guides/what-is-prompt-engineering
[3]
https://www.techtarget.com/searchenterpriseai/definition/AI-prompt-engineer
[4] https://cloud.google.com/discover/what-is-prompt-engineering
[5] https://www.ibm.com/topics/prompt-engineering
[6] https://www.akooda.co/blog/what-is-prompt-engineering
[7] https://researchguides.mvc.edu/ai/prompts
[8] https://mitsloanedtech.mit.edu/ai/basics/effective-prompts/


Harry Forsdick <http://www.forsdick.com/resume/>
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