Mastering GPT-5: Essential Prompts for Diverse AI Applications
The eagerly anticipated arrival of OpenAI’s GPT-5 has naturally sparked intense industry discussion, with the model touted for its expansive capabilities across coding, writing, image generation, and even autonomous agency. To cut through the initial hype and assess its real-world performance, a series of diverse prompts were put to the test, evaluating whether GPT-5 truly surpasses its predecessors or merely adds to the growing AI landscape.
In initial trials, GPT-5 demonstrated promising utility in structured task creation. When tasked with designing a social media tracker, the prototype flawlessly executed all requested features. It accurately assigned roles, tracked daily posting progress (four posts per platform per day), and even incorporated celebratory confetti animations upon completion. The resulting output, including a well-structured JSON format with platform-specific color codes and motivational prompts, highlighted the model’s ability to generate practical, developer-ready solutions. Similarly, for a “Guess the Word” game, GPT-5 produced a visually appealing and interactive user interface with smooth gameplay and responsive feedback. However, a critical omission was noted: the core functionality allowing the player to input a secret word for the AI to guess was absent, preventing full alignment with the original prompt. Despite this, the prototype showed considerable potential. The model also excelled in academic preparation, generating a comprehensive 10-question multiple-choice test on Agentic AI, complete with four options per question, a final score report, and detailed explanations for incorrect answers, citing relevant examples and mimicking exam conditions.
However, GPT-5’s performance notably faltered in more complex operational tasks and creative applications. An attempt to automate data collection for weekly analysis by retrieving social media posts from specific channels (Instagram and LinkedIn) after a certain date yielded incomplete results. Despite the typical volume of posts (around 4 per day per platform), GPT-5 returned significantly fewer entries, failing to capture the full dataset accurately.
The model’s reasoning and image analysis capabilities also proved disappointing. In a direct comparison with OpenAI’s earlier models, GPT-5 was tasked with identifying individuals and their associated colors within a drawing. Despite repeated attempts, even utilizing its “Thinking Mode,” the model consistently provided incorrect answers. This performance suggests that GPT-5’s reasoning abilities may not meet the high benchmarks advertised by OpenAI for such complex queries, falling short of expectations set by previous versions.
Perhaps the most significant regression was observed in image generation. When compared to GPT-4o, GPT-5 exhibited substantial shortcomings. It struggled notably with text rendering, failing to accurately incorporate or display text within generated images. The overall image quality was also noticeably lower, characterized by reduced resolution and increased artifacts. Furthermore, the model frequently misunderstood or outright ignored specific prompt requests, indicating a significant decline in prompt adherence. For a supposedly improved iteration, these regressions in core functionality are a considerable concern.
In conclusion, while GPT-5 shows competence in structured coding tasks and certain forms of content generation, its shortcomings in critical areas such as reasoning, accurate data extraction, and particularly image generation, suggest a surprising step backward for general-purpose AI assistance. The versatility and creative prowess that defined earlier ChatGPT versions appear diminished in GPT-5, leading to an underwhelming experience for users who relied on its broader capabilities beyond specialized coding. The overall lack of transparency regarding which model version is generating responses further complicates user assessment.