Generative AI and the Art of "Prompt Creation for AI Chatbot to get Results"
Summary
• Generative AI can produce surprisingly useful content from natural language prompts, but requires responsible guidance and thinking on behalf of us humans
• Crafting effective prompts is becoming its own skill - "prompt engineering" - involving techniques to constrain, direct and specialize output. It is not eally “engineering”. It’s just thinking clearly about how the tools work and how to elicit imitations appropriately.
• Large Language Models have no inherent accuracy or ethics - they risk reflecting biases and misinformation from training on internet data
• Experts debate needing more transparency and governance to address generative AI risks around deception, privacy, and bias
• Striking a balance between innovation and oversight remains contested as applications expand across sectors
Using GPTs Effectively
I love how many ideas are expressed in the cartoon that I generated about Plato and Wittgenstein having a conversation to share. Ignoring the typos, it expresses so many of their philosophies in a humorous way, right down to drawing Wittgenstein without a mouth. Think about that.
Wittgenstein would likely assess LLMs as incapable of truly complex cognition without prompts carefully establishing frameworks to elicit that thought in a directed manner. The prompt-as-instructor guides the possibilities of the LLM-as-student. Adjusting and perfecting those prompts based on interpretive feedback enables progress.
So… “We prompt, therefore LLMs think and therefore exist” with apologies to Descartes. (Ed. Wittgenstein didn’t like not agree with René either.
The latest advances in generative artificial intelligence models respond to natural language prompts with startling and unpredictable creativity. Unlike traditional software requiring precise programming instructions, tools like ChatGPT allow everyday users to unleash its powerful - and unfiltered - pattern recognition capabilities through more conversational prompts.
However, "prompt engineering" these complex systems effectively also poses risks if deployed without appropriate constraints according to experts. Crafting a "good" prompt is similar to coaxing helpful information from a search engine or a teacher through thoughtful questions and conversation - the exact words , paragraphs, mental models and parameters matter tremendously in shaping AI model output.
While a clear prompt is essential, additional guidance is necessary to keep systems secure, accurate and on track aligned to human values. Enthusiasts praise prompt engineering as a fast path to "AI literacy", but critics argue its growing prevalence paired with AI's black box design demands greater scrutiny before generative models influence real world decisions via less technical intermediaries.
Bias and Privacy Lurking Under the Hood
A core limitation with many leading generative AI systems is their reliance on massive datasets scraped from public internet sources to "train" the machine learning models that power them. Researchers note this means any historical biases around race, gender and other sensitive attributes reflected in that wider internet data inevitably seeps into influencing the AI systems' worldviews as well.
Closely related is the issue of data privacy and proper consent. Public internet data may be scraped freely today, but when repurposed to fuel profitable AI engines without notice, additional ethical questions around individual rights arise that have yet to be fully answered.
Policymakers and Researchers Debate Oversight
Some experts argue simple disclaimers about accuracy limitations and media literacy education around AI is sufficient as the technology continues maturing rapidly. But others push for more interventions given the unchecked power for abuse or deception. In Europe, the Artificial Intelligence Act proposes new requirements enforcing principles like transparency, oversight and proportionality on AI system functionality directly.
Striking the right balance between innovation and governance remains hotly debated globally. But documenting prompt engineering, templated techniques and use cases that proactively consider societal impacts help set the stage for more informed policy discussions in this fast moving domain.
