Prompt Engineering: Better Results with Less Trial and Error

You can watch this talk on YouTube in Russian and English(auto-dubbed).
The presentation is available here.


Ali Kuzhuget’s presentation focused on a foundational but often underestimated topic: prompt engineering. His central point was simple and practical — while many people treat prompting as something obvious or informal, mastering it can significantly improve output quality, reduce wasted iterations, save tokens, and lower the overall cost of working with AI systems.

The talk covered several core prompting approaches, beginning with zero-shot prompting, where a user asks for something directly without giving examples or structure. Ali explained that while this is the most common way people interact with AI systems, it often produces weak or incomplete results because the model lacks sufficient context. From there, he moved to few-shot prompting, where examples are provided so the model can infer the desired pattern, and then to more guided reasoning approaches such as chain-of-thought, where the interaction unfolds through multiple steps and clarifications until the result becomes more precise.

One of the strengths of the session was its emphasis on the fact that prompting is not universal across tools. Different models and platforms have different strengths, preferences, and output behavior. Ali used examples from image generation, music generation, video generation, and coding workflows to show that understanding the model’s “reading style” matters. A vague prompt such as “a man in a suit” may produce a generic result, while a much more structured prompt that specifies style, framing, lighting, background, camera angle, and intended output context can dramatically improve quality. The same logic applies to music, where genre, mood, structure, and musical language influence outcomes, and to video, where timing, motion continuity, and transitions become especially important.

He also spent time on the practical cost of poor prompting. Bad prompts do not just produce disappointing outputs — they consume retries, burn tokens, and waste time. That is especially painful in multimodal systems where generation is more expensive and post-processing is harder. In video generation, for example, he pointed out that prompts need to anticipate what happens in the next step of the workflow, because abrupt movement or poor ending states make clips difficult to stitch together later. This was an important reminder that good prompt design often means thinking not only about the immediate output, but also about how that output will be used downstream.

Another major theme of the talk was grounding. Ali connected prompt quality to hallucination reduction by emphasizing the importance of supplying reliable source material, system-level instructions, and structured context when factual accuracy matters. He referred to notebook-style and retrieval-grounded workflows as examples of how outputs become more stable when the model is anchored to defined information sources instead of improvising from its generic training patterns.

He also briefly touched on model settings such as temperature and sampling behavior, noting that these influence creativity, variability, and factual stability. That helped place prompting in a broader context: good outputs depend not only on the words in the prompt, but also on the model’s configuration and intended use.

The overall tone of the talk was practical and experience-driven. Ali openly reflected on the fact that trying to discover everything independently can be costly, and encouraged the audience to learn from official guides, platform tutorials, and established best practices rather than assuming prompting can be mastered purely through intuition. While the audience discussion was short, that practical framing clearly resonated: one of the immediate follow-up requests was for him to share the presentation so attendees could revisit the examples and prompting patterns afterward.