What are the best prompts for ChatGPT to get a better response?

Why Your ChatGPT Answers Feel Generic (It's the Prompt, Not the Model)

You ask ChatGPT for a blog post and get a wall of emojis, exclamation marks, and the same over-enthusiastic corporate speak every time. Then you spend ten minutes re-prompting to coax out something usable — time that a better first prompt would have saved.

The diagnosis is uncomfortable but clear: the problem is rarely the model. It's the prompt. Prompt engineer Mike Taylor explains that vague prompts return "the average of what's out there" in the training data — and that average leans heavily on emojis and corporate enthusiasm. When nobody tells the model who the audience is, what tone to use, or what format to produce, it defaults to the most generic response possible.

Weak prompts don't just produce bland output. According to contact-center AI company Parloa, "when a model hallucinates or produces a vague answer, a weak prompt is often the root cause." In other words, the quality problems you blame on ChatGPT are usually instructions you never actually gave it.

Consider what a vague prompt leaves out:

• A role or perspective for the model to adopt
• Context about your situation, audience, and goals
• A clear output format and length
• Constraints, such as tone or what to avoid

The costs add up fast. Practitioner guides describe marketing executives burning half an hour coaxing a decent social caption out of a lazy one-liner, and Equinet Academy's analysis puts it bluntly: "a lazy prompt gets a lazy answer." Re-prompting a vague request often takes ten minutes or more before anything usable appears.

There's a nuance worth acknowledging. Mike Taylor notes that today's models need far fewer prompting tricks than GPT-3 did back in 2020 — but more guidance still yields better results. The tricks matter less; the specificity still matters enormously. OpenAI's own official best practices confirm it: prompts should be clear, specific, and provide enough context, and refinement is expected to be iterative.

The good news is that this is a fixable input problem, not a model problem. Structured frameworks — giving your prompt a role, context, task, format, and guardrails — replace guesswork with repeatability. Once your prompts are solid, the same discipline scales up: agent skills can automate the rest of a workflow, and SkillzBench is building a free directory to help you discover, verify, and benchmark those skills with confidence.

The Anatomy of a High-Quality Prompt: Frameworks That Work

Most vague prompts fail for the same reason: they ask ChatGPT to guess what you want. As prompt engineering guidance bluntly puts it, when a model hallucinates or produces a vague answer, a weak prompt is often the root cause — and without a framework, every prompt is bespoke.

Across frameworks, a pattern emerges. Strong prompts consistently contain five components: role assignment, context, task clarity, output structure, and guardrails. Task clarity matters most. Parloa's example is telling: "Respond politely" fails, while "Write a two-sentence apology that references the customer's previous delayed delivery" works. OpenAI's own best practices guide confirms the same fundamentals — clarity, specificity, and enough context.

Not every task needs a heavyweight prompt. The right framework depends on what you're asking for:

• RTF (Role, Task, Format) — best for simple, well-defined tasks like "Act as a copywriter. Write a headline. In three options."
• T-A-G (Task, Action, Goal) — suits goal-oriented requests where the outcome matters more than the format.
• B-A-B (Before, After, Bridge) — designed for problem/solution strategy: describe the current state, desired state, and ask the model to bridge the gap.
• CO-STAR — the flagship for detailed, audience-aware output.

CO-STAR (Context, Objective, Style, Tone, Audience, Response) was created by GovTech Singapore's Data Science & AI team and popularized by Sheila Teo, winner of Singapore's first GPT-4 Prompt Engineering competition in 2023. Its edge over lighter frameworks is that it bakes audience, tone, and response format directly into the prompt.

Two principles make it work. First, it separates style from tone — as Equinet Academy explains, "Style decides the skeleton. Tone decides the personality wearing it." Second, it follows a single-goal principle: one prompt, one objective. You are not writing an essay to the AI; you are filling in six clear slots.

The payoff is real. Practitioners report that vague re-prompting routinely burns ten minutes or more, while marketing executives have lost half an hour coaxing out a decent caption. And as IBM frames it, prompt engineering is "the new coding" — a skill that extends naturally into agentic workflows, where packaged agent skills can automate the steps beyond the prompt itself. SkillzBench is building a free directory and MCP connector for exactly that: discovering, verifying, and benchmarking skills so you can equip your agents with confidence.

Five Evidence-Backed Techniques You Can Copy-Paste Today

Most people blame the model when ChatGPT returns generic, emoji-stuffed corporate speak. Prompt engineer Mike Taylor explains that directionless prompts produce exactly that, because the model defaults to "the average of what's out there" in its training data, as detailed in his five research-backed techniques. Four of his five methods cite ArXiv papers — here they are as copy-paste templates.

1. Role-playing. Open with: "You are an expert in [field] known for [key adjective]. Help me [task]." Assigning an expert persona steers the model away from average-sounding output toward specialist-level depth.

2. Style unbundling. Instead of vaguely asking for "good writing," extract the style you want into explicit bullet points — sentence length, vocabulary, structure — then instruct: "Do [task] in the following style." A quick shortcut: appending "in the style of [famous person]" can make a huge difference.

3. Emotion prompting — with a caveat. Adding "This task is very important for my career" can sharpen output, but Taylor cautions it can sometimes backfire and produce worse results. Test it, don't trust it blindly.

4. Few-shot examples. Show the model 2–3 examples of the output you want before giving it the new task. This is one of the most consistently endorsed techniques, also recommended in Google Cloud's official guidance alongside chain-of-thought prompting.

5. Synthetic bootstrap. Have ChatGPT generate example outputs first, then feed the best ones back as in-context examples for the real task.

The strongest quantitative evidence for structured reasoning comes from the Game of 24 benchmark: GPT-4 with chain-of-thought solved only about 4% of problems, while Tree of Thoughts reached 74%, per research cited by Parloa. How you structure the model's thinking matters as much as what you ask.

• Use descriptive tone adjectives — "friendly," "professional," "serious" — per OpenAI's official guidance.
• Separate style (structure) from tone (personality): "Style decides the skeleton. Tone decides the personality wearing it."
• Iterate: OpenAI states prompt engineering "often requires an iterative approach" — start simple, review, refine.

One honest caveat: today's models need far fewer tricks than GPT-3 did in 2020, though more guidance still yields better results. And once your prompts are solid, agent skills can automate the rest of the workflow — SkillzBench is building a free directory and MCP connector to discover, verify, and benchmark those skills, so you can test what actually helps before trusting it.

From Better Prompts to Automated Workflows: The Next Step

Mastering a single great prompt is an achievement — but the real shift happens when your best prompts stop being something you retype and start being something your AI runs on autopilot. That is where prompting is heading, and it changes how you should think about everything you've just learned.

IBM has framed this evolution bluntly: "prompt engineering is the new coding." But IBM also argues that crafting better prompts is "only the beginning" — true expertise extends into broader context engineering: retrieval-augmented generation, structured inputs like JSON, and what IBM calls agentic prompting — guiding AI agents to take autonomous actions across multistep workflows rather than answering one question at a time.

The evidence that prompting technique scales to harder problems is already visible in the research. On the Game of 24 reasoning task, GPT-4 using standard chain-of-thought solved roughly 4% of problems, while the Tree of Thoughts approach reached 74% — a finding cited in Parloa's framework analysis. Structure doesn't just polish output; it unlocks capabilities that otherwise fail entirely.

This is exactly where agent skills come in. A skill is a packaged set of instructions — typically a SKILL.md file — that teaches a coding agent or chat agent like Claude Code, Codex, Cursor, or Gemini CLI how to handle a recurring task. In effect, your best prompt becomes reusable infrastructure:

• The role, context, and constraints you perfected once get applied consistently, every time, without re-prompting
• Multistep workflows — research, drafting, review, formatting — run as one automated sequence instead of ten manual chats
• Skills can be shared across your team, so everyone benefits from the same prompt engineering quality

There's a catch, though: the skill ecosystem is growing fast with no reliable way to tell what actually works — and what's safe. Published research found that roughly 26% of published agent skills carry at least one vulnerability, which makes blindly installing one a genuine business risk.

That's the problem SkillzBench is being built to solve. Currently in pre-launch, SkillzBench is creating a free, independent directory that continuously crawls and dedupes skills from GitHub, pins every version to a commit SHA, and runs license checks, security scanning, and quality validation. Every skill gets benchmarked in a sandbox — tested with the same model, with and without the skill — so you can see the measured Lift in percentage points rather than trusting a README. A free MCP connector will then let you equip any agent with only the verified skills you choose.

Prompting got you a better answer. Skills get you a repeatable outcome — and soon, a way to know which ones are worth trusting. Join the SkillzBench waitlist to get skills, proven.