Microsoft's Azure Foundry documentation provides a comprehensive guide to prompt engineering techniques for GPT models, covering foundational concepts through advanced strategies. The article breaks down prompts into key components — instructions, primary content, examples, cues, and supporting content — explaining how each element shapes model behavior. It emphasizes that GPT models fundamentally predict the most likely next words based on training data, making prompt design more art than science. Notably, these techniques are not recommended for reasoning models like GPT-5 and o-series models.
The guide dives into practical strategies including few-shot learning, chain-of-thought prompting, output structure specification, and the use of affordances like search to ground model responses in factual data. It demonstrates how breaking complex tasks into smaller steps, repeating instructions, and using clear syntax (such as Markdown or XML separators) can significantly improve output quality. The article also covers temperature and Top_p parameters for controlling randomness, and provides grounding context techniques to reduce hallucinations.
A set of best practices rounds out the guide: be specific, be descriptive, leverage recency bias by placing instructions strategically, and give models an "out" to avoid fabricated responses. The article also addresses space efficiency, noting that tabular data formats are more token-efficient than JSON, and that careful whitespace management can help maximize the limited context window available in GPT models.
Techmeme shares an article from The Atlantic by Jasmine Sun exploring how AI's post-training process actively suppresses the creativity and whimsicality that were present in earlier language models like GPT-2. The piece argues that this refinement process — which includes reinforcement learning from human feedback (RLHF) and other alignment techniques — leads to homogenized, formulaic output, resulting in bad writing from many top AI models.
The insight highlights a tension in AI development: as models become more capable and "safe," they simultaneously lose the unpredictable, expressive qualities that made raw language models occasionally produce genuinely interesting prose. This matters for anyone relying on AI for creative work, as the post-training pipeline may be systematically trading literary quality for consistency and compliance.
Ahmad shares a self-study roadmap for deep learning broken into 5 phases, outlining hands-on milestones that learners should aim to hit. These include building an autograd engine from scratch, writing a mini-GPT from scratch, implementing LoRA and fine-tuning a model on real data, and — humorously — hating CUDA at least once and crying along the way. The advice is practical and self-paced: skip what you already know, rewatch what confuses you, and keep going when stuck. The post captures the gritty, iterative nature of learning ML engineering with a relatable, no-nonsense tone.
Ethan Mollick highlights an ongoing academic debate about AI-assisted idea generation. The discussion centers on three interconnected papers: first, an experimental study showing that even the older GPT-3.5 model helped people generate higher-quality ideas; second, a response paper arguing that the AI-generated ideas tend to be highly similar to one another, raising concerns about diversity of thought; and third, a counter-response suggesting that this lack of variety may not matter since the outcomes are still demonstrably good.
The thread captures a nuanced tension in AI research — whether the quality of individual outputs outweighs concerns about homogeneity and originality. This is a key question for anyone using large language models as brainstorming tools, as it speaks to the tradeoff between reliably strong results and the risk of converging on a narrow set of ideas.