Chrome ExtensionGemini Prompt Generator
Updated for Gemini 3.7 Flash, Google's August 2026 workhorse. Generate prompts built the way current Gemini models actually want them: concise and direct, structured with Markdown or XML, constraints first, output format explicit — and free of the forced chain-of-thought and self-critique scaffolding that a reasoning model now over-analyses.
Be specific about what you want the AI to do
Generated Prompt
Fill in the form and click "Generate" to create an optimized Gemini prompt.
Tip: The more specific your task description and context, the better the generated prompt will perform.
Gemini Tips
- • Be CONCISE — Gemini 3.x is a reasoning model and over-analyses verbose, over-engineered prompts
- • Skip persuasive filler ("please", "if you don't mind", "this is very important") and redundant restatement
- • Don't write "think step by step" to force reasoning — raise the thinking level and simplify the prompt instead
- • Skip the self-critique ritual too: the model already deliberates internally, so it mostly adds tokens and hedging
- • Use Markdown headings (## Role, ## Task, ## Output Format) OR XML tags — pick one and stay consistent
- • Place role and constraints at the TOP; with long pasted material, put it first and bridge with "Based on the material above…"
- • Gemini defaults to terse output — state the format and the length as a number if you want more
- • For Workspace tasks, reference files by name (Gmail thread, @Drive doc, Docs file) so Gemini binds to the right source
Gemini Prompt Templates
Copy-ready starting points for the most common jobs people bring to this model. Swap the [BRACKETED] parts for your own details, or use them as a shape to imitate when you write your own.
Concise Technical Answer
EngineeringShort and direct — the shape current Gemini models reward most.
## Role [ROLE, e.g. Senior platform engineer] ## Task [THE SPECIFIC QUESTION OR TASK] ## Context Stack: [STACK AND VERSIONS]. Scale: [SCALE]. Constraint: [HARD CONSTRAINT]. ## Output Format [FORMAT, e.g. numbered steps with a code block per step]. Maximum [NUMBER] steps.
Long Document Q&A
AnalysisUses the 1M window: material first, question after.
[PASTE THE FULL DOCUMENT, TRANSCRIPT OR CODEBASE HERE] --- Based on the material above, answer: [QUESTION] ## Constraints - Cite the section or line each answer comes from. - If the material does not cover something, say so plainly. ## Output Format [FORMAT, e.g. a table with Question / Answer / Source columns]
Workspace Synthesis
ProductivityBinds Gemini to named files so it synthesises the right sources.
## Task Using the @Drive document "[DOC NAME]" and the Gmail thread with [PERSON/SUBJECT], produce [DELIVERABLE]. ## Context This is for [AUDIENCE] ahead of [EVENT/DEADLINE]. ## Constraints - Pull facts only from the named sources; flag anything you cannot find in them. - Keep names and figures exactly as written in the sources. ## Output Format [FORMAT, e.g. a one-page brief with three H2 sections], under [WORD COUNT] words.
Design Mock to Code
EngineeringPlays to Gemini 3.7 Flash's web-development and spatial reasoning strengths.
## Role Front-end engineer ## Task Build the attached design as a [FRAMEWORK, e.g. React + Tailwind] component. ## Constraints - Match spacing, type scale and colour to the mock; do not improvise a different layout. - Responsive down to [BREAKPOINT]. - Semantic HTML, keyboard accessible, no external UI libraries. - [LIGHT/DARK/BOTH] theme. ## Output Format One self-contained component file in a fenced code block, followed by a short list of any judgement calls you made where the mock was ambiguous.
Multimodal Review
AnalysisNames the uploaded artefacts explicitly so the model binds to them.
## Task Review the [IMAGES / SCREENSHOTS / PDF] I uploaded and identify [WHAT TO FIND]. ## Context These are [WHAT THEY ARE]. The audience is [AUDIENCE] and the purpose is [PURPOSE]. ## Constraints - Reference each finding to the specific file and region it came from. - Rank findings by impact. ## Output Format Numbered list. For each: what you saw, why it matters, what to change.
Structured Data Extraction
DataTerse instruction plus an explicit schema — no scaffolding needed.
## Task
Extract [WHAT] from the content below into JSON.
## Schema
{"[field1]": "string", "[field2]": "number|null", "[field3]": "string[]"}
## Constraints
- JSON only, no commentary.
- null where the source is genuinely silent. Never infer.
## Content
[PASTE CONTENT]Gemini Model & Prompting Update Log
Gemini 3.7 Flash
● LatestAugust 13, 2026- Google's generally available workhorse model for coding and agentic work, released less than a month after Gemini 3.6 Flash.
- Keeps the 1,048,576-token context window and a 64K-token output limit.
- Substantial gains on software engineering and automation — Google-reported DeepSWE v1.1 rose from 49.0% to 65.3% over Gemini 3.6 Flash, with AutomationBench at 30.4%.
- Positioned for software engineering, web development from design mocks, multi-step agentic workflows, and spatial and multimodal reasoning.
- Introductory pricing of $0.75 per million input tokens and $3.75 per million output runs through December 31, 2026, then doubles to $1.50 and $7.50 on January 1, 2027.
How prompting Gemini changed
Gemini 3.x guidance- Gemini 3.x models are REASONING models, and Google's guidance is to be concise and direct — they over-analyse the verbose prompt-engineering scaffolding that helped on older models.
- Reasoning depth moved to a thinking level (low, medium, high) set outside the prompt. Low suits latency-critical work like real-time chat and fast drafts; medium is the default and best for most tasks including complex code and agents; high is for hard maths, difficult coding and the toughest agent runs.
- If you previously used chain-of-thought prompt engineering to force reasoning, Google's advice is to replace it with a higher thinking level and a SIMPLER prompt.
- On the API side, temperature, top_p, top_k and candidate_count were dropped, thinking_budget was replaced by the thinking_level enum, and prefilled model turns are no longer supported.
Earlier 2026 releases
June–July 2026- Gemini 3.6 Flash reached GA on July 21, 2026 with improved token efficiency for code and agentic planning, alongside Gemini 3.5 Flash-Lite as a low-latency, high-volume subagent option.
- Gemini Omni Flash entered public preview on June 30, 2026 for video generation and editing.
- Gemini 3.5 Pro, the flagship promised at I/O, remained in limited preview on Vertex AI rather than launching publicly.
How to Use the Prompt Generator
Define Your Task
Select a category and describe what you want Gemini to do. Assign a role, set reasoning depth, and pick your structure format (Markdown or XML). Be direct and precise — Gemini rewards clarity over flowery filler.
Set Structure & Constraints
Add context, choose tone and output format, and set constraints. The generator will use Markdown headings and structured formatting that Gemini interprets best.
Generate & Use
Click "Generate" to get a structured Gemini-optimized prompt. Copy and paste it directly into the Gemini app, AI Studio, or your Workspace tools for dramatically better results.
Frequently Asked Questions
What is a Gemini prompt generator?
A Gemini prompt generator helps you create optimized prompts specifically designed for Google's Gemini models. It structures your inputs following Gemini-specific best practices — concise, direct instructions, structured formatting with Markdown or XML tags, constraint-first placement, and explicit output format — which matters more than ever now that Gemini 3.x models reason internally and penalise padded prompts.
How is prompting Gemini different from ChatGPT?
Gemini favours directness over verbosity and produces terse output by default, so you often have to ask explicitly for detail. It responds best to structured prompts using Markdown headings or XML tags, with constraints at the very top. The bigger difference on Gemini 3.x is that it is a reasoning model that actively over-analyses elaborate prompt scaffolding — the techniques people learned on older models (forced chain-of-thought, self-critique steps, heavy role-play framing) now work against it. Gemini is also unusually strong on multimodal inputs and Google Workspace integration.
When should I split into multiple prompts?
When you have several genuinely distinct deliverables. Gemini works best conversationally with one focused deliverable per turn, so producing them in sequence beats cramming them into a single request. Use this generator for each focused deliverable, then chain them in conversation. Note this is about splitting TASKS, not about fragmenting the context — with a 1M-token window you should paste long source material in full, then ask your question after it.
What is the thinking level and which one should I pick?
Gemini 3.7 Flash lets you trade latency against depth with three thinking levels. Low reduces time-to-answer for latency-critical work — real-time chat, incident response, quick drafts, fast data analysis. Medium is the default and gives the best quality for most tasks, including complex code and agentic use, with higher first-pass accuracy. High maximises reasoning and tool use, and is worth it for hard maths, difficult coding and the toughest agent runs. Because depth is set here rather than in the prompt, the prompt itself should stay short and specific.
What is the current Gemini model?
Gemini 3.7 Flash, generally available since August 13, 2026, is Google's workhorse model for coding and agentic tasks. It carries a 1M-token context window with 64K output, and improved sharply on software engineering over Gemini 3.6 Flash — Google-reported DeepSWE v1.1 went from 49.0% to 65.3%. Introductory pricing of $0.75 per million input tokens and $3.75 per million output runs through the end of 2026. Gemini 3.5 Pro, the flagship announced at I/O, has stayed in limited preview rather than launching publicly.
Is this tool free to use?
Yes. You get 1 free prompt generation per day with no signup required. For unlimited access, sign up for a Promptslove membership which includes all AI tools and 20,000+ premium prompts.
Can I use these prompts with other AI tools?
While optimized for Gemini, the structured prompting approach works well with Claude, ChatGPT, Grok, MiniMax, Qwen, and most modern LLMs. The Markdown/XML structure and constraint-first pattern are model-agnostic.
What is structured prompting?
Structured prompting uses Markdown headings or XML-style tags to organize different sections of your prompt (identity, constraints, context, output format). This provides unambiguous boundaries that help Gemini distinguish between instructions and data, leading to more accurate and consistent results.
Should I use self-critique directives?
No — this is advice that has aged badly. It helped on older Gemini models, but the current 3.x generation already deliberates internally before answering, so "review your output against the constraints and revise" mostly adds tokens and makes the response more hedged. The same applies to "think step by step" added purely to force reasoning. If a task needs more thought, raise the thinking level to high and SIMPLIFY the prompt — that is Google's own guidance.
How many prompts can I generate for free?
You get 1 free prompt generation every 24 hours. This resets automatically. For unlimited generations, consider signing up for a Promptslove membership.
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