Every few months, someone posts the same question in r/AI_Agents: what are the actual best practices for AI prompting right now, across tools like ChatGPT, Claude, Gemini, and NotebookLM? One recent thread asking exactly that pulled in a genuinely useful mix of answers on AI prompting, from quick tips to a few detailed breakdowns of how experienced users structure their prompts.
This is a sentiment roundup based on that community discussion, not an independently verified guide. A couple of tools and blog links mentioned in the thread read as self-promotion rather than neutral recommendations, and we’ve flagged those below rather than treating every AI prompting tip with equal weight.
Quick Answer
| Approach | What It Means | What Redditors Said |
|---|---|---|
| Let AI write the prompt | Ask the model itself what information and structure it needs before writing your real AI prompt | Called “the single biggest tip” by one commenter |
| Structure over keywords | Frameworks and modular design beat “magic keyword” lists in AI prompting | Multiple users said most generic “best prompt” lists don’t hold up in real use |
| Break prompts into steps | Chain smaller, sequential instructions instead of one long prompt | Consistently more accurate results, according to several commenters |
| Understand the model, not just the prompt | Learn roughly how training, RL, and a tool’s underlying “harness” shape AI prompting outcomes | Framed as more valuable long-term than memorizing techniques |
| Version and test your prompts | Keep a personal, versioned AI prompting library and test across models | Preferred over browsing “best prompt” lists by experienced users |
| Know your source | ChatGPT, Claude, or Gemini’s chat interface handles AI prompting differently than their APIs, since the system prompt differs | Flagged as an easy thing to overlook |
What Is AI Prompting?
AI prompting is the practice of writing the input, context, and instructions you give a language model to get a useful, accurate response. It covers everything from a single quick question to an elaborate, multi-step structure with defined roles, examples, and constraints. As this thread makes clear, effective AI prompting in 2026 has moved well past typing a request and hoping for the best, it’s increasingly treated as a skill with real structure behind it.
The Case Against Prompt Templates: Let the Model Help You Prompt It
One of the most upvoted AI prompting tips in the thread was refreshingly simple: skip the manual entirely. As one commenter put it, “there is no manual to writing prompts, you let AI write the prompts.” The original poster tried this directly, asking ChatGPT to help design a prompt structure collaboratively: what information the model needed, how it wanted a prompt formatted, and what details it valued most. The model responded with a structured breakdown covering context, goals, constraints, and format, essentially co-designing its own ideal prompt.
A separate commenter described a more elaborate version of the same idea: using deep research to compile a report on AI prompting frameworks and techniques, feeding that report to a model as a knowledge base, and then having the model generate a detailed, well-structured prompt from it. They iterated on that output until satisfied, then used the refined prompt to build an actual prompt-generation agent, describing the end result as “a godsend.”
Structure Beats Magic Keywords in AI Prompting
A recurring theme across replies: the “type a paragraph and pray” era of AI prompting is over, and so is the era of collecting lists of clever phrases. One detailed comment broke down a framework nicknamed “DEPTH”: Define the role, Establish goals, Provide context, outline Task steps, and build in a Human-loop for self-critique, essentially treating a prompt as a small system rather than a single request.
The same commenter emphasized a few related AI prompting ideas that came up elsewhere in the thread too:
- Chain prompting, breaking one large task into a multi-turn sequence (plan, draft, refine, adjust) instead of one monster prompt.
- Data-aware context, feeding the model concrete examples or constraints (tone, length, target metrics) up front rather than describing them abstractly.
- Feedback prompts, asking the model to rate or critique its own output before you do, which several users said noticeably speeds up iteration.
Worth noting: this same commenter also linked to their own blog several times while making these points, which reads more like content promotion than a neutral citation, so we’re passing along the underlying ideas without vouching for the specific source.
Break Big AI Prompting Tasks Into Smaller Steps
Several commenters converged on a simpler, less framework-heavy version of the same advice: break prompts into smaller, sequential instructions rather than one long block of text, which multiple users said produced noticeably more accurate results than a single dense prompt.
One user described using Traycer, a tool built to turn a plan into step-by-step instructions that coding assistants like Claude Code and Cursor can follow, as part of a structured AI prompting workflow. In their setup, the tool generates prompts for a coding agent based on the existing codebase, asks clarifying questions when something’s ambiguous, and later verifies the agent’s work and generates feedback that gets cycled back for fixes.
Another commenter took a more manual approach, prompting directly from the terminal and keeping versioned prompts so they could compare what actually worked across different models, arguing that most published “best prompt” lists don’t hold up once you test them yourself.
Understand the Model, Not Just the Prompt
The most philosophically dense reply in the thread argued that AI prompting technique is secondary to understanding how the underlying model actually works: how it was trained, how reinforcement learning shaped its behavior, and how a tool’s “harness” (the surrounding system and subsystems a company builds around a model) affects what a prompt can and can’t do. Their core point: rather than copying prompting tricks that worked for someone else’s setup, it’s more durable to understand your specific model and harness well enough to figure out the most effective approach yourself, since AI prompting techniques that work today will likely need to evolve as models get more capable.
A smaller but practical point from elsewhere in the thread reinforces this: prompting a model directly through ChatGPT, Claude, or Gemini’s own chat interface can behave differently than prompting it through the API, because the underlying system prompt (the instructions layered on top of your input before the model ever sees it) differs between the two.
Where to Find AI Prompting Resources and Frameworks
A few resources came up as reasonable starting points for AI prompting, rather than replacements for building your own approach:
- PromptHub, a platform for collaborative prompt management, run by a user (Dan Cleary) who’s also written publicly about AI prompting research.
- OpenAI’s GPT-5 prompting guide, an official cookbook resource for AI prompting with that specific model family.
- FlowGPT and PromptHero, both mentioned as places to browse prompt ideas, with the caveat from the same commenter that the real skill is learning to guide a model with clear context and goals, not copying prompts wholesale.
- The awesome-ai-system-prompts GitHub repository, referenced by the original poster as one of the few genuinely useful AI prompting resources they’d found, a curated collection of real system prompts used by tools like ChatGPT, Claude, and Perplexity.
What Reddit Actually Agrees On About AI Prompting
A handful of points held up across the whole thread:
- Generic “best prompt” lists get diminishing returns. Multiple commenters said these don’t hold up once you actually test them, and that building your own tested, versioned AI prompting library beats browsing someone else’s.
- Structure and iteration matter more than specific wording. Frameworks, step-by-step breakdowns, and feedback loops came up far more than any specific phrase or “magic keyword.”
- Letting the model help design the prompt is underused. Several commenters treated this as one of the highest-leverage, least-known AI prompting tips in the thread.
- Context (interface vs. API, model vs. harness) changes what “good AI prompting” even means. The same prompt can behave differently depending on where and how it’s sent.
A Quick Caveat
This particular thread had more sponsored content mixed in than usual, including ads for unrelated products that appeared alongside genuine comments. We excluded those entirely from this roundup. Within the organic discussion, one commenter repeatedly linked to their own blog while sharing otherwise reasonable AI prompting advice, which we’ve noted rather than treated as a neutral citation. A couple of niche tool and resource names elsewhere in the thread were too vague to verify (one comment referenced a “CLI” tool with a name we couldn’t clearly make out or confirm), so we left those out rather than guess at what was meant.
Bottom Line
The clearest signal from this thread isn’t a specific technique, it’s a shift in mindset about AI prompting overall. Instead of hunting for a perfect prompt template, treat AI prompting as an iterative, structured process: give the model clear context and goals, break large tasks into smaller steps, ask the model to help design or critique its own prompts, and keep a versioned record of what actually works for your specific use case and model. The people getting the best results in this thread weren’t the ones with the longest prompt library, they were the ones who understood why their prompts worked.
Frequently Asked Questions
What is AI prompting, exactly?
AI prompting is the practice of writing input, context, and instructions to get a useful response from a language model. It ranges from a single simple question to a structured, multi-part prompt with defined roles, examples, and constraints. Effective AI prompting has increasingly become about structure and iteration rather than finding one perfect phrase.
Is it actually a good idea to ask an AI model to write my prompts for me?
Based on this thread, yes, and it’s arguably underused as an AI prompting technique. Several commenters described asking a model directly what context, format, and information it needed to perform well, then using that as the basis for a real prompt. This works because the model can often describe its own ideal input more precisely than a generic guide can.
Do AI prompting techniques work the same way across ChatGPT, Claude, and Gemini?
Not exactly. One commenter pointed out that prompting a model through its own chat interface behaves differently than prompting it through the API, because each product layers its own system prompt on top of your input, and that system prompt varies by product and changes over time. General AI prompting structure (clear context, defined goals, step-by-step tasks) tends to transfer across models, but exact wording and formatting tricks don’t always carry over.
What is “chain prompting” and is it worth doing?
Chain prompting is an AI prompting technique that breaks a large task into a sequence of smaller prompts (plan, draft, refine, adjust) instead of trying to get everything right in one long prompt. Several commenters in this thread found it produced more accurate, controllable results than a single dense prompt, particularly for complex or multi-step tasks.
Are curated “best prompt” lists and AI prompting libraries actually useful?
They’re a reasonable starting point, but multiple experienced commenters in this thread were skeptical of relying on them long-term. The more common advice was to use existing prompt libraries or frameworks as inspiration for your own AI prompting approach, then build and version your own set of prompts based on what you’ve actually tested against your specific tasks and models.
Do I need to understand how large language models are trained to get good at AI prompting?
Not strictly, but one of the most detailed comments in the thread argued it helps significantly. Understanding roughly how a model was trained and how a tool’s surrounding “harness” shapes its behavior can help you figure out why certain AI prompting approaches work, rather than just copying techniques that worked for someone else’s specific setup.
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