u/UltraPrompt

Hey everyone,

I'm Sean, the developer behind Ultra Prompt (a browser-based visual canvas for building and iterating on complex prompt pipelines with nodes, sequencing, templates, etc.). I have built this app to help the entire span of AI users! It's built to meet you where you are and help you grow to where you want to be.

To give back to the communities that have helped me level up my own prompting, I'm offering to craft custom prompts or multi-step pipelines for free for the first 20 people who reply this week. Please keep the requests appropriate, I'd like to give back in a way that will genuinely help your development!

What you'll get

-------------------

• A ready-to-copy, high-quality prompt (or chained workflow) tailored to your specific use case
• Explanation of the structure and why each part is there
• Insight into how I built it visually (this is where the tool shines for complex stuff)

To participate, just reply with as much detail as you can about:

-----------------------------------------------------------------------

• What you're trying to achieve
• Which model(s) you're using (Claude, GPT, etc.)
• Any context, constraints, tone, or output format requirements
• What "success" looks like for you

The hype around LLMs often focuses on the architecture and training data, but I think we're missing a fundamental shift. AI isn’t a mathematical computational tool (in the sense that you use specific mathematical equations or series to get end results), despite being helpful for arithmetic; It's increasingly apparent that it operates well as a "language" tool. The intimidation gap we’ve always felt with technology stems from the expectation that 'we' need to learn its language (i.e. 1's & 0's). The beauty of modern AI is it’s starting to meet us where we are, understanding vague & informal prompts.

My hypothesis is that prompt engineering shouldn’t be about tricking the model, but rather about effectively communicating the necessary context. Think of it less as crafting complex syntax and more like briefing an intern... provide enough background information that they can deliver exactly what you want. The "bad prompt vs good prompt" examples (vague request vs specific context) are strikingly effective.

I'm seeing this in my own work as well, and it really re-frames the problem. Instead of tweaking parameters, we're focusing on clarity of communication. What strategies have you found most effective in providing context, and how does it change the way we approach prompt design? I'd be interested to hear your thoughts, especially on scaling this approach across diverse use cases. I've had lots of great input over the last few weeks on these types of posts and genuinely believe this is beneficial for all, I am fully aware there are many experts in this realm but like to keep topics for all user levels fresh!

The conventional wisdom around using LLMs like GPT, Claude, Grok, Perplexity and Local Models focuses on crafting the "perfect" initial prompt. While prompt design is crucial, I've been seeing far more significant gains by shifting focus to an iterative prompting "framework" designed for deep collaboration.

The core idea is moving away from a one-off query and toward a structured conversation, leveraging techniques to force the LLM beyond its default "helpful assistant" persona and into a more critical, reasoning-focused role.

My current framework involves three stages: Contextualization, Reasoning Request, and Iterative Challenge.

1. Contextualization: This goes beyond just stating the topic. It involves explicitly outlining your current understanding, previous attempts (and their failures), and desired outcome. The goal is to provide sufficient grounding for the LLM to generate relevant insights – essentially, minimizing superficiality.

2. Reasoning Request: Instead of asking for a solution, ask for its *reasoning*. Prompts like "Walk me through your thought process on this” or “What assumptions are you making?" dramatically increase the depth of exploration.

3. Iterative Challenge: This is where most users drop the ball. Don't accept initial outputs as definitive. Employ contrasting prompts: "What’s a counterargument to your claim?", “Build the strongest possible case [against] this decision." Also key is using follow-up questions like "Which aspect of that solution aligns best with X?", or "How would this change if Y was different?" to continuously refine the AI's perspective.

I’ve seen particularly good results using role-playing prompts ; Assigning the LLM a specific persona (e.g., "Act as an experienced marketing consultant") to shape its responses and expose blind spots. Now there are tons of different frameworks out there, so whatever follows the role people assign it (If that's the first set of criteria in your framework) is obviously crucial too.

The power here isn't in writing a single, complex prompt but rather creating a repeatable process for escalating the LLMs cognitive abilities. Anyone else exploring similar iterative approaches? What is your favorite Framework to use and why?

If you're a small business owner or solopreneur, I bet you feel like you’re constantly chasing your tail. Decision fatigue can kill productivity and impact everything from client work to marketing efforts. I was spending a ridiculous amount of time just 'figuring out' what to do each morning, before I even started working.

I’ve been experimenting with using AI to structure my mornings, or at least as a reference, and the results have been surprisingly impactful. Instead of scattering my focus across a dozen tasks, I'm now following a plan that prioritizes deep work and recovery, that I customized myself using AI models. It’s freed up 2+ hours a week, which I'm now reinvesting into client projects and my business.

The best part? It’s not about automating everything, but more so about streamlining the initial planning phase. There are a few simple prompts that I use to generate a daily sequence, saving me valuable mental energy. Even if you’re not tech-savvy, a little bit of AI prompting can unlock serious time savings. If getting your mornings under control is on your to-do list, it's worth a quick look.

I've been deep diving into building a personalized morning routine using LLMs, specifically focusing on how structured input can dramatically improve output quality. The core challenge is moving beyond vague requests ("Help me with my morning") to solicit concrete action plans. My exploration involved contrasting naive prompts (e.g., "Write a list of things I should do this morning") with highly structured prompts incorporating user context (energy level, priorities, constraints).

The key difference wasn’t just the output; it was how clarifying those initial parameters, essentially defining your own problem before requesting a solution, created a mini "mental warm-up." The prompts used currently revolve around: 1) Contextual Planning (defining goals, constraints), 2) Task Sequencing (prioritization and time blocking), 3) Adaptive Iteration (weekly reviews using AI to identify patterns & suggest adjustments).

I'm particularly interested in how "wait for my answers" instructions within conversational prompts enhance user engagement and introduce a feedback loop. I've found that this technique, in combination with ongoing prompt refinement using iterative weekly reviews (feeding AI past morning check-ins to identify trends) provides a surprisingly robust foundation. Any thoughts or recommendations on further refining this methodology, particularly exploring techniques for dynamic context updates throughout the day?