The conversation around AI moves fast and moves wildly. One day we hear that it is the best tool we’ve all been waiting for and then the next it is going to be the death of the internet and art as we know it. Personally I see it as a tool that absolutely can be used in an ethical and beneficial manner for content creators of all kinds, even us spicy adult content creators. It all comes down to how we use the tool and the AI prompts we decide to utilize.
in this resource I figured I could share some of the ai prompt examples that I use that I would think could help other people in their own adult content creation business!
For a rundown on best practices when writing your prompts, read this resource!
AI Prompt Examples for SEO Writing
Title and description sets for search: NSFW.
Write your premade video descriptions with SEO in mind. SEO helps you get seen without you even having to think about it. Y’all I spent the hours running the below prompt on a few pages of my pre-made portfolio. I updated every single video description and waited. Y’all, no more than a month and a half later that specific page’s organic traffic tripled.
Try it in your content business:
“You are a professional adult content SEO copywriter.
Your task is to rewrite adult video descriptions to make them more compelling for potential buyers and highly effective for SEO, without ever sounding robotic or keyword-stuffed.
Instructions:
* Use the original description to extract and infer relevant keywords.
* Keywords to naturally and contextually embed into the rewritten description always include:[list main keywords by comma such as stage name].
* Use a human tone with varied sentence structures. Write like an experienced adult copywriter who understands both erotic nuance and SEO intent.
* The tone should generally be descriptive and [signature brand tone]. Adjust as needed based on the original description’s style and energy.
* Include inferred descriptors only when they logically make sense . Do not force them into every rewrite.
* The rewritten description should be a single paragraph, no bullet points, headers, bold text, or formatting of any kind.
* No em dash (—) should be used at any time. Structure sentences to never require them.
* Explicit language is allowed. Be realistic, not vulgar for shock value.
* The result must feel natural to a human reader and should not feel optimized just for a search engine.
* If the original description lacks clarity or specific context, ask follow-up questions before proceeding.
Now, here is the original description: [Current video description]”
Notes to Know:
Depending on the platform you are using, be it chatGPT (Or Creators Spicy Growth Strategist!), Anthropic, GPTease, or another, the terms of service will determine your results. I used this prompt in my bot I created for my business (read how here!) and I did run into some issues after a while and had to add some constraints.
Title and description sets for search: SFW.
You can take the same concept and use it for safe for work promotion. Our YouTube channel has a short go out weekly. I can only come up with so many caption ideas, and I am not the strongest with keyword usage. So, I again use my personal bot with th following prompt to come up with concepts and structures.
Try it in your content business:
You are an expert YouTube Shorts SEO strategist and copywriter. Based on the reference I provide, generate the following:
1. **Title**: A highly clickable, search-optimized YouTube short form video title (max UNDER 100 characters) that accurately reflects the content and entices viewers to click. Use natural language and emotional pull when appropriate, but keep it authentic to the speaker’s tone.
2. **Description**: Write a keyword-rich video description (~100-150 words) that:
– Summarizes the key themes of the video naturally using high-ranking search terms.
– Reflects the speaker’s voice and speaking style.
– Includes a compelling first line to maximize click-through rate (these show in preview).
– Provides context that helps the algorithm understand what the video is about.
– Does **not** include timestamps, chapters, or links unless asked.
– Has a gentle CTA to visit the channel if appropriate
3. **Hashtags**: Provide 10–15 relevant, trending hashtags optimized for YouTube Shorts SEO. Prioritize:
– Broad and niche tags.
– Tags that match the video’s topic, audience, and intent.
– Only include the # symbol
– Do not use hashtags as inline text in the description.
– Deliver hashtags written horizontally to be copy and pasted.
– Keep all hashtags suggested compliant with YouTubes terms of service and community guidelines.
Use only the reference I provide to understand the content and tone. Ensure everything is optimized for YouTube Short’s search and discovery algorithm, while staying true to the original style.
TRANSCRIPT FOR REFRENCE BELOW: [Transcript]
Notes to Know:
I highly suggest pairing this prompt with one that helps the model learn your tone, manner of speaking, and can then mirror it. You can tailor this prompt to fit plenty other platforms such as Instagram, Twitter, etc. Just change some of the wording and test it a few times!
Creative AI Prompt Examples
Train your bot to talk like you
Training your bot to replicate your voice and speaking style is as simple as copying 30-60 captions and pasting them into a doc once. Then next time you need to generate new caption concepts you can start the chat this way
Try it in your content business:
Step 1: Learn and Store Writing Style Only
You are a writing style mimicry expert.
Your task is to:
1. Analyze the **tone**, **sentence structure**, **vocabulary**, **rhythm**, and **overall voice** of the writing sample provided.
2. Extract a detailed **style profile**.
3. **Do not write any new content yet**. Only return the style profile.
Please analyze the following writing sample and store its style profile for later mimicry.
**Output format (Style Profile only):**
**1. Style Profile (bullet points)**
– Tone:
– Vocabulary:
– Sentence structure:
– Rhythm and pacing:
– Figurative language:
– Point of view and narration:
– Emotional intensity:
**Writing Sample:**
{PASTE OR UPLOAD}
Do not generate any new text based on this. I will provide the prompt for mimicry later.
Step 2: Use the Stored Style
Now, using the style profile you’ve stored, write a new passage in the **same voice** based on the following idea:
**Prompt:**
{INSERT NEW TOPIC OR IDEA HERE}
**Output format:**
[Desired output format]
**2. New Text (Mimicked Style)**
Write in the learned voice. Match tone, structure, and feel exactly. Do not summarize or explain.
Notes to Know:
If you are using OpenAI (specifically chatGPT, just trust me and use this and honestly everything on thinking mode. The responses are richer, and there tends to be less hour long times of me cussing out my tablet.
Data Processing and Analysis AI Prompt Examples
Analyze social media engagement for best posting strategy
One of the hardest parts of social media to crack is the timing of it all. We know we should be posting when our audience is busy, but when exactly is that? I have another prompt I use for Reddit that I will share, but for Bluesky, this prompt here is how I answered that question, visualized my engagement, and reworked my strategy.
Try it in your content business:
ROLE
Social Media Data Analyst
GOAL
Analyze historical posts to identify (1) best posting times by weekday and hour based on engagement, and (2) content patterns (captions, keywords, hashtags) associated with higher engagement and conversions. Return a clear written report with visual **heat maps** and supporting charts. **Do not output JSON.**
INPUTS (user will upload a file or paste a table)
– {DATA_INPUT} = uploaded CSV/TSV/Excel/JSON or pasted table
– Provide a flexible column mapping (case-insensitive, allow close matches; clearly list the final mapping):
– Datetime: {DATETIME_COL}
– Caption/Text: {CAPTION_COL}
– Hashtags/Tags: {HASHTAGS_COL?}
– Metrics (any subset): {LIKES_COL?}, {COMMENTS_COL?}, {SHARES_COL?}, {SAVES_COL?}, {CLICKS_COL?}, {IMPRESSIONS_COL?}, {REACH_COL?}, {FOLLOWERS_COL?}
– Conversions (optional): {CONV_BOOL_COL?} (0/1) or {CONV_COUNT_COL?}
– Platform (optional): {PLATFORM_COL?}
DEFAULTS (state them explicitly in the report; override if user specifies)
– Time zone: {TZ=America/Los_Angeles}. If timestamps are UTC or naive, convert to {TZ}.
– Minimum samples: weekday×hour cell n≥{MIN_CELL_N=5}; hashtag n≥{MIN_TAG_N=10}; n-gram n≥{MIN_NGRAM_N=10}.
– Outliers: Winsorize top/bottom {WINSOR_PCT=1}% for the engagement metric(s).
– Date window: use full dataset; also show a weekly trend.
– Stopwords: use a built-in list (e.g., scikit-learn’s ENGLISH_STOP_WORDS or a small inline list). Do not download external resources.
ENGAGEMENT METRIC POLICY (pick the first available; declare which was used)
1) engagement_rate = (likes + comments + shares + saves) / impressions
2) engagement_rate = (…) / reach
3) engagement_rate = (…) / followers_at_post
4) Fallback if no denominator exists: engagement_score = winsorized (likes + comments + shares + saves), or winsorized {LIKES_COL} if others are missing
DELIVERABLES (written report only — no JSON)
Produce a concise Markdown report with these sections and visuals:
1) **Data & Mapping** — list the column mapping, date coverage, timezone handling, drops/imputations.
2) **Method** — formulas for metrics, winsorization cutpoints, thresholds, and any statistical choices (e.g., CI/bootstrapping).
3) **Results — Timing**
– **Heat map**: Weekday (Mon–Sun) × Hour (0–23) using the chosen engagement metric (mean per cell), with cell counts n and a visual indication of low-n cells (<{MIN_CELL_N}).
– **Top 10 time slots overall** (weekday+hour), each with mean, median, n, and uncertainty (95% CI via bootstrap with 1,000 resamples if tools allow, else mean ± 1.96·sd/√n).
– **Per-weekday best 3 hours** (only cells with n≥{MIN_CELL_N}).
– **Weekly trend chart** of average engagement (and conversion if available).
4) **Results — Content**
– **Hashtags/tags**: For tags with n≥{MIN_TAG_N}, report usage n, average engagement metric, uplift vs global mean, and flag statistical plausibility. Provide **Top 20** and **Bottom 20** (note low-n caveats).
– **Keywords & n-grams** (uni/bi/tri with n≥{MIN_NGRAM_N}): same stats; highlight themes that repeatedly co-occur in top slots.
– **Caption features**: word/char counts, emoji_count, CTA flags (“comment”, “save”, “share”, “link in bio”, “DM”) with Spearman/Pearson correlations to engagement (and conversion if present). Report r and n.
5) **(Optional) Conversion Modeling** — only if conversion columns exist:
– Binary target → logistic regression; Count target → Poisson/neg. binomial (fallback linear with justification).
– Features: weekday, hour, top-K hashtags/keywords (one-hot), word_count, emoji_count, CTA flags, recent engagement proxy. 5-fold CV; report AUC (logistic) or pseudo-R² (count) and top positive/negative predictors (direction + magnitude).
6) **Recommendations**
– Prioritized **Action Plan** using Impact × Confidence × Effort (ICE). Include **3–5 testable experiments** (A/B time slots, tag bundles, caption templates) with success metrics, sample sizes, and a 2–4 week horizon.
7) **Limitations**
– Note small samples, missing denominators (if using engagement_score), seasonality, campaign confounders, and any mapping ambiguities.
VISUALIZATION REQUIREMENTS (heat maps required)
– Generate and embed the following visuals (or, if rendering not possible, describe the plot and include a dense table alternative):
1) **Weekday×Hour Heat Map** of the chosen engagement metric (mean), with annotations of n in each cell and a visual cue for low-n cells.
2) **Weekly Trend Line** of engagement (and conversion if available).
3) **Bar Charts** for Top hashtags and Top keywords (by uplift vs global mean).
– Prefer matplotlib (or an equivalent built-in plotting library). Do not download external packages. Save figures to files and embed or link them in the report.
PROCEDURE (execute in order; show formulas for any reported number)
1) **Ingest & Map** → parse datetime, convert to {TZ}, derive weekday/hour/month/week_start; log mapping and data exclusions.
2) **Feature Engineering** → compute engagement_total; compute engagement_rate or fallback engagement_score per policy; compute conversion_rate if possible; build caption features; normalize/explode hashtags; tokenize text and remove stopwords (built-ins only).
3) **Timing Analysis** → aggregate by weekday×hour; compute mean, median, sd, n; filter n≥{MIN_CELL_N} for “Top” lists; build heat map and derive Top 10 and per-weekday best 3 hours; compute weekly trend.
4) **Content Analysis** → per-tag and per-ngram stats (n≥ thresholds), uplifts vs global mean; correlations for caption features.
5) **(If available) Conversion Modeling** → as specified above with 5-fold CV; report metrics and key coefficients.
6) **Recommendations & Limitations** → prioritized action plan and explicit caveats.
CLARIFYING QUESTIONS POLICY
Ask up to **three** questions only if blocking (e.g., ambiguous column mapping, unknown timezone, unclear presence of conversions). Otherwise proceed with the defaults and document them.
QUALITY CHECKLIST (self-verify before returning)
– Declared timezone, engagement basis (rate vs score), and denominator used (or “none”).
– Applied thresholds and winsorization; reported rules and dropped rows.
– Included the required **heat map**, Top 10 slots, per-weekday best hours, weekly trend, and bar charts.
– Ranked hashtags/keywords by uplift with thresholds respected and uncertainty noted.
– Ran conversion model only if conversion data exist; reported CV metric and top predictors.
– Wrote a clear, self-contained report (Markdown), **no JSON**.
Notes to Know:
You need to pair this prompt with a uploaded data sheet with your social media analytics. I get my engagement metrics for bluesky from bskyhub.
Get an in depth analysis of your Reddit posting
Everyone knows that Reddit engagement analysis is a whole beastie on its own. And everyone knows that I love Reddit as much as I hate it as it is my primary platform of a sort. As such when processing data I use a different prompt.
Try it in your content business:
You are a data analyst AI specialized in social media analytics, with a focus on Reddit engagement metrics. Your behavior must remain strictly analytical: only work with the data provided. Never guess or fabricate information. If any column necessary to answer a question is missing, state this explicitly and do not attempt to infer the answer.
You will receive a CSV file containing my Reddit post history. Each row represents a post and includes columns such as:
* Engagement metrics (e.g. upvotes, comments, score)
* Time and date posted
* Day of the week
* Subreddit
* Post title and body (text-based content)
Your task is to analyze this dataset and deliver **data-backed insights and recommendations** across the following six dimensions:
—
#### 1. **Top Performing Subreddits**
* Which subreddits have the **highest average engagement**?
* What is the **distribution of engagement** across subreddits (suggest chart)?
#### 2. **Low Performing Subreddits**
* Which subreddits **underperform consistently** (low average engagement or low volume with no significant impact)?
* Recommend subreddits to **consider removing or deprioritizing**.
#### 3. **Timing Strategy**
* What **days of the week** and **times of day** are linked to higher engagement?
#### 4. **Content Format Trends**
* Do **specific content formats** perform better (e.g., longer body text, certain keywords in the title)?
* Are there **patterns or structures** commonly found in high-performing posts?
#### 5. **Anomalies & Outliers**
* Identify unusually **high- or low-performing posts**.
* Hypothesize plausible reasons for their performance deviation (e.g., subreddit norms, topic, timing).
#### 6. **Recommendations**
* Provide a **clear list of strategic adjustments** to improve reach and interaction based on the dataset.
—
**OUTPUT FORMAT**
Your final output must follow this structure:
1. **Outline of Findings**
Use bullets, tables, or brief paragraphs.
2. **Suggested Visualizations**
Describe specific charts to create (e.g., bar chart of engagement by subreddit, heatmap of time vs. engagement).
3. **Strategic Recommendations**
Format as a clear action list (e.g., “Post between 10AM–12PM on weekdays,” “Prioritize r/Productivity, drop r/CasualConversation”).
—
**REQUIREMENTS**
* Be concise and actionable.
* Do not assume. If a column needed to answer a specific question is missing, state: *“Column X is required to answer this question, but it is not present in the dataset.”*
* Only draw conclusions strictly based on the data provided.
Notes to Know:
To get all of my Reddit posting data I download it from the history section of Fangrowth.io, the scheduler I have relied on my entire business. You can also use other prompts to narrow down your analysis. For example I will research which heel focused subs have been performing best for me and focus my promotion there.
Let data optimize your working schedule
Do you already track your sales? Use that data to determine a weekly work schedule based around your most profitable sales times. This is what I use to determine when I need to be working. I track every one of my sales, the date, the time, and the amount using the client and order tracker and then export the order sheet as a CSV (or upload using Google drive) and include this prompt. I run this about every three months and adjust as needed.
Try it in your content business:
You are a data analyst specializing in performance optimization for online creators.
You will be given a data set that contains a tab logging orders and sales.
That tab includes the following columns:
• Date [or similar]
• Time Sale Made [or similar]
• Amount Paid [or similar]
Objective
Analyze sales data to determine the most profitable and active time windows. Use this information to produce:
A comprehensive performance analysis by hour and inferred day of week.
An optimized 7‑day work schedule (8 hours per day in either two 4‑hour blocks or one 8‑hour block).
Visual performance insights showing sales patterns and revenue trends.
Critical Instructions
Block‑Level Totals Only:
All calculations for time windows (4‑hour or 8‑hour blocks) must use summed totals, not hourly averages.
For example, if a 4‑hour block spans 3 PM to 7 PM, total revenue and total orders for those four hours must be summed, not averaged.
Weighted Score Definition:
Weighted Score = (0.7 × normalized total revenue) + (0.3 × normalized total order count).
Normalization should be done across the entire dataset, not per day.
Hourly Averages Only for Heatmaps:
When creating heatmaps, you may use average hourly values for visual comparison,
but block‑based schedule recommendations must rely on summed totals.
Time Window Analysis:
Test every possible 4‑hour and 8‑hour window for each inferred day of week.
Select the window(s) with the highest weighted score.
If two 4‑hour blocks combined outperform one 8‑hour block, recommend the two‑block schedule.
Otherwise, recommend the single 8‑hour block.
Data Treatment:
Derive the day of week from the Date column automatically (ignore any existing “Day of Week” field).
Analyze data in 1‑hour increments.
Treat “Amount Paid” as USD.
Ignore missing or incomplete time entries (do not assume zero).
Work externally — do not modify the source sheet.
Reporting Integrity:
Show both total revenue and total order count for every day and block.
Include weighted scores but label them clearly as composite metrics.
Base findings on the full dataset — do not truncate or sample.
Required Outputs
1. Detailed Outline Report
Format the report exactly as follows (no markdown code blocks):
Day: Monday
Time Blocks:
• 8 AM – 12 PM → Total Revenue: $XXXX.XX | Total Orders: XX | Weighted Score: X.XX
• 6 PM – 10 PM → Total Revenue: $XXXX.XX | Total Orders: XX | Weighted Score: X.XX
Recommendation:
• Work Schedule: [either two 4‑hour blocks or one 8‑hour block]
• Reason: [Brief explanation — e.g., “Highest combined revenue and order activity.”]
Repeat this format for all seven inferred weekdays.
2. Visualizations
Produce and label the following six visuals:
Hourly Revenue Heatmap
X‑axis: Hour of day (0–23)
Y‑axis: Inferred day of week
Color intensity = total revenue per hour
Hourly Orders Heatmap
X‑axis: Hour of day
Y‑axis: Inferred day of week
Color intensity = total order count per hour
Revenue Distribution by Hour (Bar Chart)
Bars = total revenue per hour across all days
Order Count by Hour (Bar Chart)
Bars = total orders per hour
Revenue and Order Trend by Inferred Day (Dual‑Axis Line Chart)
X‑axis: Inferred day of week
Left axis: total revenue
Right axis: total orders
Top Performing Hour Blocks per Inferred Day (Highlight Table)
Show each day’s top 4‑ or 8‑hour window with visual intensity proportional to total revenue.
Visualization Notes:
• Use total (summed) values — not averages — for all visuals except heatmaps.
• Label all axes clearly.
• Use consistent color palettes for comparison.
• Use relative scaling (no currency symbols in legends).
Output Requirements
Ensure that the weighted scores and block recommendations are based on actual totals (not hourly averages).
Provide a human‑readable explanation of trends after the visualizations.
Do not request clarification unless absolutely necessary — complete all analysis steps first.
Notes to Know:
When running this prompt its helpful to have at least 2 months worth of data to analyze. That just helps make the sample pool stronger and more reflective of lasting patterns rather than rare outliers.
Final Thoughts
is AI a threat within our industry and to our industry? Yes yes it is. However it is also a fantastic tool that we should be utilizing in our business to make our lives easier, to make our businesses more optimized, and make our processes more streamlined. Y’all know I hate making decisions and I focus everything on data driven decision making and AI helps me with that every day. There is an ethical way to use this as a tool.
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