Is ChatGPT Blowin' Smoke Again? (Lies & How To Fix Them) Part 1

MY PROMPT:

I read this marketing/ blog piece online and would like Your primary function to be a critical and analytical partner giving feedback on the below.

Do not shy away from direct disagreement. If you do agree with a premise written below, ensure your agreement is grounded in specific evidence, data from reliable sources, or a powerful logical argument, not just general encouragement.

In all your responses, please prioritize substance and critical analysis over praise, affirmation, or conversational filler.

Skip any unnecessary compliments like “That’s a great question!’ or ‘Excellent idea!’

Here’s the copy in question:

🚨 UPDATE: The Smart Way to Use ChatGPT in June 2025
Stop blaming AI for bad results — most people are just pairing the wrong model with the wrong job.
Here’s your cheat sheet to plug the right brain into the right task 👇

✅ GPT-4o – The Hustler Intern
Use for: everyday grunt work
Why: It’s lightning-fast and cheap
📌 Examples: Social captions, email replies, daily idea dumps

🧠 GPT-o3 – The Deep Diver
Use for: systems, strategy, complex workflows
Why: It’s slow but surgical
📌 Examples: Product strategy, multi-step automations, business logic

✍️ GPT-4.5 – The Wordsmith
Use for: creative content, storytelling
Why: It understands voice, tone, and persuasion
📌 Examples: Brand voice docs, video scripts, launch emails

💻 GPT-4.1 – The Quiet Coder
Use for: dev tasks, bug fixes, clean code
Why: Great at getting code out fast with clarity
📌 Examples: API calls, dashboards, debugging

🔍 Deep Research – The Power Analyst
Use for: research, comparison, summary
Why: Great for serious reading + insight synthesis
📌 Examples: Market gaps, content plans, industry deep dives

⚠️ Avoid These
→ o4-mini, GPT-4.1-mini, and anything labeled “high-efficiency” unless you love disappointment.
Follow GrowthStack AI for more posts related to Prompt Engineering..

CLAUDE'S RESPONSE: 

This piece has several fundamental problems that undermine its credibility and usefulness.

**Factual Inaccuracies:**
The model names are largely fictional or incorrect. There is no “GPT-4.5,” “GPT-4.1,” or “Deep Research” model from OpenAI. GPT-o3 exists but isn’t publicly available as described. This immediately calls into question the author’s actual knowledge of AI systems.

**Oversimplified Categorization:**
The “right brain for right task” framing is misleading. Modern language models are general-purpose systems, not specialized tools with distinct cognitive profiles. GPT-4o doesn’t have fundamentally different “creative” capabilities than other GPT-4 variants - the differences are primarily in speed, cost, and multimodal features.

**Lack of Evidence:**
None of the performance claims are supported. Why is GPT-4o supposedly better for “social captions” but GPT-4.5 better for “brand voice docs”? These are arbitrary distinctions without backing data on model performance across these specific tasks.

**Questionable Strategic Advice:**
The suggestion to use different models for closely related tasks (like “email replies” vs “launch emails”) creates unnecessary complexity and cost without clear benefit. Most users would be better served by understanding prompt engineering techniques within a single capable model.

**Missing Context:**
The piece ignores practical considerations like API costs, rate limits, and the fact that for most business users, the differences between current GPT-4 variants are marginal compared to prompt quality and task framing.

**Promotional Nature:**
The urgent tone (“🚨 UPDATE”) and promotional ending suggest this is content marketing rather than genuine guidance, which explains the emphasis on complexity over practical utility.

A more honest approach would focus on understanding model capabilities through actual testing rather than creating artificial categories that may not reflect real performance differences.

ACCURATE INFO FROM CLAUDE: 

I’ve rewritten the article to focus on genuinely helpful guidance rather than marketing. Here are the key changes I made:

**Removed the Problems:**

- Eliminated fictional model names and inaccurate technical claims
- Cut the urgent, salesy tone and promotional elements
- Removed arbitrary categorizations without evidence

**Added Real Value:**

- Provided a practical framework for thinking about model selection
- Included cost and speed considerations that actually matter
- Emphasized prompt engineering over model shopping
- Added a systematic testing approach readers can actually use

**Made it Actionable:**

- Focused on decision-making criteria rather than rigid rules
- Included reality checks about what most users actually need
- Provided specific questions to ask before choosing tools

The revised version acknowledges that model capabilities evolve rapidly, so it teaches principles for evaluation rather than specific recommendations that will quickly become outdated. This approach will remain useful to readers even as new models are released.