Prompt Tips & Tricks
Practical tips and tricks for writing better prompts for AI tools like ChatGPT, Claude, and Midjourney.
53 articles
-0124.png&w=3840&q=75)
Perplexity AI: How to Write Search Prompts That Actually Pull the Right Sources
A practical way to prompt Perplexity like a research assistant: tighter questions, better constraints, and built-in verification loops.
-0123.png&w=3840&q=75)
How to Write Prompts for Grok (xAI): A Practical Playbook for Getting Crisp, Grounded Answers
A developer-friendly guide to prompting Grok: structure, constraints, iterative refinement, and how to test prompts like a product.
-0122.png&w=3840&q=75)
Best Prompts for Llama Models: Reliable Templates for Llama 3.x Instruct (and Local Runtimes)
Prompt patterns that consistently work on Llama Instruct models: formatting, role priming, structured outputs, and safety-aware prompting.
-0121.png&w=3840&q=75)
GPT-5.2 Prompts vs Claude 4.6 Prompts: What Actually Changes (and What Doesn't)
A practical, prompt-engineering comparison between GPT-5.2 and Claude 4.6: where wording matters, where it doesn't, and how to write prompts that transfer.
-0119.png&w=3840&q=75)
Google Gemini Prompts: The Complete Guide for 2026
How I write reliable Gemini prompts in 2026: system instructions, long-context hygiene, multimodal patterns, and agent-ready tool calls.
-0118.png&w=3840&q=75)
How to Write Prompts for AI Music Generation (That Don't Sound Like Random Loops)
A practical, engineer-friendly way to prompt AI music tools for structure, instrumentation, and controllable results.
-0117.png&w=3840&q=75)
AI Prompts for Real Estate Listings That Don't Sound Like AI
A practical prompt system for crisp, compliant, high-converting real estate descriptions-plus templates you can reuse across MLS, Zillow, and social.
-0116.png&w=3840&q=75)
Best Prompts for Social Media Content Creation (That Don't Sound Like a Bot)
A practical prompt pack for better hooks, threads, carousels, and repurposed posts-plus the prompt-engineering rules that make them work.
-0115.png&w=3840&q=75)
How to Use AI Prompts for Academic Research (Without Getting Burned by Hallucinations)
A practical, prompt-first workflow for literature, synthesis, and writing-plus guardrails to keep citations and claims verifiable.
-0114.png&w=3840&q=75)
Prompts for Business Plan Writing with AI: A Practical Workflow That Doesn't Sound Like a Bot
A prompt-by-prompt system to draft, stress-test, and tighten a business plan with AI-without hallucinated markets or fluffy strategy.
-0113.png&w=3840&q=75)
How to Write Prompts for AI Code Generation (So You Get Mergable Code, Not Demos)
A practical, developer-first way to prompt code models: turn vague intent into specs, constrain output, and iterate with tests.
-0112.png&w=3840&q=75)
Best AI Prompts for Learning a New Language (Without Wasting Hours Chatting)
A practical set of AI prompt templates to practice speaking, fix grammar, build vocabulary, and stay consistent-grounded in prompt-engineering research.
-0111.png&w=3840&q=75)
ChatGPT Prompts for Data Analysis and Excel: The Playbook I Actually Use
A practical prompt library for cleaning data, writing Excel formulas, building pivots, and generating SQL-plus how to make outputs reliable.
-0110.png&w=3840&q=75)
How to Write AI Prompts for Email Marketing (That Don't Sound Like AI)
A practical, prompt-engineering approach to generating on-brand, high-converting email campaigns with LLMs-without generic fluff.
-0109.png&w=3840&q=75)
Best Prompts for Writing a Resume with AI (That Don't Sound Like AI)
A practical prompt library for ATS-friendly, human-sounding resumes-plus a workflow that keeps the model from inventing experience.
-0108.png&w=3840&q=75)
How to Structure Prompts with XML and Markdown Tags (So They Don't Rot in Prod)
A practical way to make prompts readable, testable, and harder to break using XML-style sections plus Markdown fences.
-0107.png&w=3840&q=75)
RAG vs Prompt Engineering: Which One Do You Actually Need?
RAG and prompt engineering solve different failure modes. Here's how to choose, when to combine them, and what "good" looks like in production.
-0106.png&w=3840&q=75)
Prompt Chaining for Complex Tasks: Build Reliable Multi-Step LLM Workflows
A practical way to split messy requests into verifiable steps, reduce drift, and ship complex LLM features with less prompting drama.
-0105.png&w=3840&q=75)
Tree of Thought Prompting: A Step-by-Step Guide (with real prompts you can copy)
A practical, developer-friendly walkthrough of Tree-of-Thought prompting: how to branch, score, backtrack, and ship better reasoning.
-0104.png&w=3840&q=75)
Self-Consistency Prompting: How Majority-Vote Reasoning Beats Your Best Single Answer
Self-consistency prompting samples multiple reasoning paths and votes on the final answer. Here's how it works, when it helps, and prompts you can steal.
-0103.png&w=3840&q=75)
Meta Prompting: How to Make AI Improve Its Own Prompts (Without Fooling Yourself)
A practical, research-grounded way to have an LLM critique, rewrite, and regression-test your prompts-plus when meta prompting backfires.
-0102.png&w=3840&q=75)
Role Prompting That Actually Works: How to Get Expert-Level Answers (Without the Fluff)
Role prompting isn't "act as an expert." It's a way to set scope, standards, and failure modes so the model reasons like a specialist.
-0101.png&w=3840&q=75)
System Prompt vs User Prompt: What's the Difference (and Why It Actually Matters)
System prompts set the rules of the assistant; user prompts request the task. Here's how the two interact, fail, and how to use them well.
-0100.png&w=3840&q=75)
Context Engineering: the real reason prompt engineering is getting replaced
Context engineering shifts the focus from clever wording to building the right context pipeline-memory, tools, retrieval, and constraints.
-0099.png&w=3840&q=75)
Zero-Shot vs Few-Shot Prompting: When to Use Each (and Why It's Mostly About Risk)
A practical guide to choosing zero-shot or few-shot prompts, grounded in in-context learning research and real evaluation patterns.
-0098.png&w=3840&q=75)
GenAI & Creative Practices: Stop Treating Prompts Like Spells
A practical, opinionated guide to using GenAI in real creative work-where intent evolves, outputs drift, and process beats prompting.
-0097.png&w=3840&q=75)
Gemini AI Prompting: The 5 Prompt Patterns That Actually Hold Up in Real Work
A practical guide to prompting Gemini for reliable results: iterative refinement, JSON contracts, adversarial review, multi-model critique, and tool loops.
-0096.png&w=3840&q=75)
How to Reduce ChatGPT Hallucinations: Make It Cite, Verify, or Shut Up
A practical playbook to cut LLM hallucinations using grounding, verification loops, and refusal-by-default prompts.
-0095.png&w=3840&q=75)
How to Make AI Creative (Without Begging It to "Be Creative")
A practical, evidence-backed playbook for getting more novel, surprising, and useful outputs from AI using structure, sampling, and evaluation.
-0094.png&w=3840&q=75)
How to Research With AI (Without Getting Burned by Hallucinations)
A practical workflow for using LLMs as research assistants: plan, search, verify, synthesize, and keep an evidence trail you can trust.
-0093.png&w=3840&q=75)
How to Speak With AI: Treat Prompts Like Interfaces, Not Wishes
A practical way to talk to AI models: specify intent, add constraints, invite clarifying questions, and iterate like you're debugging an API.
-0092.png&w=3840&q=75)
Prompt to Make Money: Stop Chasing "Magic Prompts" and Start Building Revenue Prompts
A practical way to write prompts that reliably produce sellable work-offers, proposals, research, and negotiation scripts-grounded in real prompt design research.
-0091.png&w=3840&q=75)
10 tips for writing image prompts that actually control the output
A practical, developer-friendly guide to writing image prompts with clear constraints, fewer surprises, and faster iteration.
-0090.png&w=3840&q=75)
10 tips for writing video prompts that actually follow your intent
A practical, model-agnostic way to prompt text-to-video so you get controllable shots, consistent subjects, and fewer rerolls.
-0089.png&w=3840&q=75)
How to Prompt Nano Banana (Gemini 3 Pro Image): The Few Patterns That Actually Matter
A practical way to prompt Nano Banana for image generation and editing-without cargo-culting camera specs or fighting the model.
-0088.png&w=3840&q=75)
How to Prompt the Best Way (Without Turning It Into a Weird Ritual)
A practical, evidence-backed way to write prompts that ship: clear goals, strong context, tight output contracts, and an iterative loop.
-0087.png&w=3840&q=75)
What Is a Prompt? The Input That Turns an LLM Into a Tool
A practical definition of "prompt" for developers-plus what actually belongs in one, why it works, and how to write prompts that don't fall apart.
-0086.png&w=3840&q=75)
How to Generate Images in 2026: Prompting Like a System, Not a Poet
In 2026, great image generation is about constraints, iterative edits, and tool choice-not vibes. Here's a practical workflow and prompts.
-0085.png&w=3840&q=75)
The Latest LLM Prompt Updates (Early 2026): What Changed, Why It Matters, and How I'd Update My Prompts
Early-2026 prompt changes aren't about clever phrasing-they're about evaluation loops, structured outputs, and surviving model upgrades without prompt drift.
-0084.png&w=3840&q=75)
How Prompts Changed in 2026: From Clever Wording to Testable Systems
In 2026, prompting stopped being copy-paste poetry and became an engineering discipline: evals, security boundaries, and prompt-as-code.
-0083.png&w=3840&q=75)
ChatGPT prompt for photo editing: the only template I use (and why it works)
A practical, developer-friendly prompt template for editing real photos with ChatGPT-plus examples for retouching, background swaps, and product shots.
-0082.png&w=3840&q=75)
How ChatGPT Works (Without the Hand-Wavy Magic)
A practical, engineer-friendly tour of what's under the hood: tokens, transformers, attention, decoding, and why alignment changes what you see.
-0081.png&w=3840&q=75)
Keeping Context in a Prompt: The 3-Layer Pattern That Stops Long Chats From Derailing
A practical way to preserve context in prompts using a stable brief, rolling memory, and focused task blocks-without blowing your token budget.
-0080.png&w=3840&q=75)
How to Keep Context in a Prompt (Without Writing a Novel)
A practical, system-level approach to preserving context: pin what matters, summarize what doesn't, and route memory on purpose.
-0079.png&w=3840&q=75)
How to Write Prompts for Claude 4.5: A Practical Playbook for Getting Reliable Outputs
A developer-friendly guide to prompting Claude 4.5 with structure, guardrails, and iteration loops that actually hold up in real work.
-0076.png&w=3840&q=75)
How to Write Prompts for Sora 2: The Spec That Turns "Cool Video" Into Something You Can Ship
A practical, developer-minded way to prompt Sora 2: treat prompts like specs, lock constraints early, iterate in layers, and avoid the usual drift.
-0075.png&w=3840&q=75)
How to Write Prompts for Veo 3: A Developer's Playbook for Getting the Shot You Actually Want
Veo 3 prompting isn't poetry-it's spec-writing. Here's how I structure prompts to control subject, camera, motion, and style reliably.
-0073.png&w=3840&q=75)
How to Write Video Prompts That Actually Direct the Camera (Not Just Describe a Vibe)
A practical, opinionated framework for writing text-to-video prompts: story beats, shot specs, motion rules, and iteration loops.
-0078.png&w=3840&q=75)
What Is Prompt Engineering? A Practical Definition (and Why It's Not Just "Asking Nicely")
Prompt engineering is the craft of designing inputs, constraints, and feedback loops so LLMs behave reliably. Here's what it is and how it works.
-0072.png&w=3840&q=75)
What Is Prompt Engineering? A Practical Definition (and Why It's Not Just "Writing Better Prompts")
Prompt engineering is the discipline of designing, testing, and maintaining prompts as interfaces to LLM behavior-like programming, but in natural language.
-0071.png&w=3840&q=75)
AI prompts vs. generative AI prompts: the difference that actually changes your outputs
Most "AI prompts" are requests. Generative AI prompts are specs. Here's how to think about the difference and write both types on purpose.
-0070.png&w=3840&q=75)
Chain-of-Thought Prompting in 2026: When "Think Step by Step" Helps (and When It Backfires)
A practical, opinionated guide to chain-of-thought prompting-why it works, where it fails, and how to use it without getting fooled.
-0069.png&w=3840&q=75)
How to Write Prompts for ChatGPT: The Only Structure I Use (and Why It Works)
A practical, developer-friendly way to write ChatGPT prompts that stay on-task, reduce drift, and produce usable outputs.