ChatGPT & Dall•E: Sell Your Creative Thinking with AI

Explore the strengths and limitations of generative AI and learn how to distinguish appropriate uses at work. Shape the future of creativity with responsible AI choices.

Train AI to produce content at scale in seconds, using ChatGPT and Dall-E to create brand-voice aligned product descriptions and social posts, guided by copywriters for fast, consistent output.

Revise your original content with AI by summarizing and rewriting from another character’s perspective, then edit images with Dall-E to match client feedback.

Use AI to break through creative block by generating first drafts and endless variations. Explore with Dall-E's 'surprise me' and 'variations' features to spark inspiration, test directions, and styles.

Explore the limitations of AI in generating and revising creative work, its potential usefulness in the creative process, and the ongoing improvements and major version updates.

AI models pull from a finite knowledge base, so even large databases may fail on prompts needing real-time data. ChatGPT's data ends in September 2021, limiting real-time discussion.

Assess AI output reliability by tracing information provenance and its impact on credibility, and learn to verify sources, cite evidence, and conduct independent research with real-time data.

Understand plagiarism and content provenance risks in ai outputs and identify bias from training data. Use plagiarism detectors, reverse image search, and bias checks before publishing.

AI content requires manual refinement after generation, with DALL·E often misplacing typography and text, and ChatGPT producing natural but imperfect writing that benefits from human editing and design.

Learn how AI apps can glitch or go down, highlighting AI's early-stage nature. Plan timelines with breathing room for technical difficulties, including paid tiers that deprioritize free users.

AI works in a vacuum; text and art must collaborate to create a greater whole, while AI lacks context and continuity, requiring human guidance.

Master prompt engineering by applying four simple tips to craft prompts that reliably elicit higher quality output from generative AI, recognizing that input drives output and control the algorithm.

Be specific in prompts by detailing format, subject, tone, and guardrails to guide ai output, and apply your human understanding and subject matter expertise to compare two levels of specificity.

Describe the subject and the style to guide AI prompts, using general artistic terms rather than specific artists, boosting results and addressing bias as a quick proof of concept.

Provide context in prompts to shape output; be specific, creative, and descriptive, giving character details and a clear direction to guide ChatGPT and Dall-E toward more original results.

Iterate beyond the first AI prompt to push the concept toward more creative, smarter, on-brand outcomes. Act as AI's creative director, adding your originality to bring your vision to life.

Explore why AI systems are biased, stemming from human perspectives and biased training data, and learn to recognize and mitigate biased outputs in real-world apps like GPT-4 and Firefly.

Explore how bias infiltrates AI systems through real-world cases like Amazon's hiring tool and biased facial recognition, and learn why diverse training data matters.

Address AI bias in creative work by designing detailed prompts to counteract bias, evaluating outputs for assumptions, and applying diversity training and developer feedback to improve models.

Explore three AI legal buckets—data sets, tools, and outputs—and learn how copyright, licensing, ownership, and human authorship affect creative work with AI.

Learn how ownership, authorship, and licensing rights evolve with AI tools like Midjourney and ChatGPT, and why reading terms, drafting contracts, and documenting prompts protect creators.

Navigate the ethics and legalities of style references in AI art and text tools, and learn to blend styles while executing your own ideas.

Explore intentional plagiarism in AI-assisted creation, examine ownership debates, and learn to protect originality by screening candidates, mentoring teams, clarifying AI roles, and conducting upfront research.

Develop transparent AI guidelines early, label all AI-generated work, and align your team on how these tools should be used to avoid plagiarism, bias, and ownership issues.

Compare free and paid AI app plans, including ChatGPT with GPT-4, Dall•E credits, and Midjourney subscriptions, and weigh ownership terms, plagiarism checks, and AI-detection tools for professional use.

Budget for AI training resources beyond app costs by consulting an IP attorney, a DEI consultant, and an ethical technology specialist to navigate evolving laws, address bias, and uphold transparency.

address the risk of ai devaluing creative work by selling creative thinking as the main deliverable, highlighting critical thinking, context evaluation, and emotional understanding to create relatable, valuable briefs.

Define the idea and its execution as the backbone of great creative work, and note how AI tools like ChatGPT and Dall-E lack the human insight that gives originality.

This case study contrasts idea and execution using Heinz's campaign, showing how human insight drives the idea while AI aids the execution with Dall-E.

Harness ai tools like Dall-E and ChatGPT to free up time, focus on developing the concept, and preserve the human touch—empathy, gut instincts, and passion.