“Most People are saying GPT – 4/5 are trash” – But maybe the Real Sauce lies somewhere else

Reevaluating Perceptions of GPT-4 and GPT-5: Are They Truly Flawed or Is the Approach the Key?

Recently, a recurring sentiment has emerged across various online communities: many users consider GPT-4 and GPT-5 to be underwhelming or even “trash.” While such criticisms are often rooted in casual interactions with the models—where simple prompts yield generic or watered-down responses—it’s essential to delve deeper into what’s really happening behind the scenes.

Understanding the Evolving Nature of These Language Models

Several factors contribute to the current perception of these AI models:

• Enhanced Safety Measures: To ensure responsible AI use, the models have been optimized to produce less edgy and more neutral responses by default. While this increases safety, it can also reduce the models’ willingness to generate more nuanced or controversial content.

• Increased Size and Complexity: GPT-4 and GPT-5 are significantly larger than their predecessors, which, in turn, implies that unlocking their full potential requires more sophisticated prompting techniques and structural guidance.

• Generalist Capabilities: These models are designed to serve a wide range of applications and users. If you’re seeking highly specialized or surgical precision outputs, you’ll likely need to incorporate external tools or chained prompting strategies.

Demonstrative Comparison: Casual Versus Structured Prompting

To illustrate this, I conducted a simple side-by-side test:

• Casual Prompt: Asked the models to generate a basic, generic essay on a topic. The output was standard, somewhat bland, and lacked depth.

• Structured Chain Prompting: Used a layered approach, guiding the model through specific steps—such as outlining key points, requesting detailed analysis, and synthesizing information. The result resembled a thorough research assistant’s work, with more clarity and depth.

The Bigger Question: Are the Models “Broken” or Are We Using Them Incorrectly?

Rather than dismissing GPT-4 and GPT-5 as underperformers, it may be more productive to ask ourselves if we need to evolve our prompting techniques. These models aren’t necessarily flawed; they’re powerful tools that require a strategic approach to unlock their full capabilities.

Final Thoughts

The landscape of AI language models is shifting, and so should our expectations and methodologies. If we adapt how we engage with GPT-4 and GPT-5—employing more structured, layered prompts—we may find that they deliver results far beyond surface-level responses.

Are you noticing similar changes