Prompt Playground

Prompt Engineering: What Actually Works (Without the 8-Hour Hype) I’ve seen people drop 8-hour-long videos on prompt engineering, and honestly, my reaction is 🤦‍♂️. I won’t bore you with the obvious stuff or overcomplicate things. Instead, I want to share a few practical techniques that actually helped me write better prompts, some common sense, some hard-earned lessons. Most of what I’m sharing comes from the book Hands-On Large Language Models So here’s what I’ve learned that actually works: 1. Specificity This one seems obvious, but it’s also the most commonly missed. A vague prompt gives you a vague answer. The more precise you are about your goal, format, and constraints, the better the result. Bad Prompt: `Write something about climate change.` Good Prompt: `Write a 100-word summary on how climate change affects sea levels, using simple language for a high school audience.` See the difference? Specific inputs = Specific outputs. 2. Hallucination Guardrail We all know that LLMs hallucinate, they confidently make stuff up. A surprisingly simple trick: Tell it not to. Try this prompt: `If you don’t know the answer, respond with ‘I don’t know.’ Don’t make anything up.` This becomes really important when you're designing apps or knowledge assistants. It helps reduce the risk of wrong answers. 3. Order Matters This was a surprise to me and I learned it from the book. Where you place your instruction in a long prompt matters. Either put it right at the start or at the end. LLMs often forget what’s in the middle (especially in long prompts). Example: Here's a paragraph. Also here's a use case. Here's some random info. Now summarize. `Summarize the following paragraph:" [then the content]` Simple shift, big difference. Other Techniques That Help Me Daily 1. Persona: Set the role clearly. `You are an expert Python developer who writes clean code.` This changes the behavior completely. 2. Audience Awareness: My favorite when I want to simplify things. `Explain this like I’m five.` Works brilliantly for breaking down tough concepts. 3. Tone: Underrated but essential. Want a formal reply? `Write this in a professional tone for a client. vs Make this sound like I’m texting a friend.` 4. Instruction / Context: Always useful. `Summarize the following news article in bullet points.` Gives the model direction and expected output format. 5. Grammar Fixing: As a non-native English speaker, this one’s gold for me. `Fix the grammar and make it sound more natural.` It has helped me immensely in writing better content, emails, blogs, even this post :-) These are the techniques I use regularly. If you have your own prompt engineering hacks, I’d love to hear them, drop them in the comments! vgrichina: u/berrrybot let's do a demo app to explore these prompting ideas use llama by default but allow to switch to other models available via [pollinations.ai](http://pollinations.ai)