A friend of mine hates emojis.
She can’t stop them from creeping into Claude or ChatGPT outputs—and it drives her nuts.
I’m the opposite. I like them. They give me quick visual markers for tone and structure while prompting.
Turns out, LLMs like them too.
From Reddit threads to X debates, emoji-filled sources dominate large-scale training sets.
And the downstream effect? Your AI assistant speaks like the internet. But this isn’t just about tone. It’s about data. And data is destiny in AI.
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Do LLMs Care About Emojis?
Why emoji overload in AI outputs is just the tip of a deeper data quality issue.
Data is the real secret to AI success. Not just having data—but having the right kind. Clean, structured, business-aligned data is what separates high-performing enterprise AI from tools that confuse, hallucinate, or undermine trust.
Too often, companies obsess over prompts and ignore their data inputs. But in reality, prompt engineering is just performance tuning. Data is the engine.
One place this shows up? Emojis.
It may seem minor, but emoji behavior in LLMs reveals exactly what your model has been taught—and whether it's learning from Reddit, Slack threads, or your internal strategy memos. If your AI output sounds off, it probably is. And it’s your data that’s to blame.
Let’s look at why emoji use is a telltale sign—and how to build AI that speaks your language.
It’s easy to blame AI for acting strange. Maybe your assistant adds 🤯 to a sales summary or inserts 🔥 into your annual report. But these aren’t glitches—they’re symptoms of a deeper issue. Models only know what we teach them. And most of the time, that means learning from data that was never designed for your use case.
Recent research reveals that emojis aren’t just decorative output—they’re part of the LLM learning process.
From Text to Emoji shows that after PEFT fine‑tuning (using QLoRA) on personality traits, large LMs like LLaMA‑2‑7B‑chat and Mistral‑7B begin generating emojis even though emojis were not in the fine‑tuning data.
Interactions between text content and emoji types determine perceptions of both messages and senders shows that presence/type of emoji + sentence valence interact to change how people perceive emotional tone, clarity, warmth etc., even when text is the same.
Semantics and Sentiment (Crosss‑lingual Variations in Emoji Use) studies how people from different language/cultural backgrounds (English, Portuguese, Chinese) differ in literal vs. figurative use of emojis, and how that correlates with sentiment of context.
Performance Evaluation of Sentiment Analysis (on Text and Emoji Data Using End-to-End, Transfer Learning, Distributed and Explainable AI Models (Velampalli, Muniyappa, Saxena, 2025)) compares performance of models on tweets + emoji data. Shows that when validation includes emojis not in training, accuracy drops; but overall, inclusion of emojis improves or changes the sentiment classification behavior.
Emoji use isn’t random. It’s a learned behavior based on data volume and tone correlation. It also seems to emerge even when the training data doesn’t include emojis. Let’s look at how training works.
How AI Training Works
When models are trained on Reddit, Slack, and Discord threads—emoji usage gets embedded as a tone signaling habit. As a result:
Summaries mimic informal posts.
Outlines are littered with bullets like 📌 and ✅.
Even corporate responses get 😬 or 🔥 if tone isn't governed by internal data.
This isn’t just about tone. It’s a data issue.
Your AI is mirroring the structure, norms, and signals of the data you feed it. So when emojis show up where they shouldn’t, it’s not because the model is improvising—it’s because the internet is in its bloodstream.
How To Get Rid of Emojis
Before you fix your prompts, you need to fix your inputs. Emoji-laden outputs are just the surface symptom. The deeper issue is that most models are trained—or fine-tuned—on data that wasn’t built for your business tone, culture, or communication needs.
These four implementation steps help you realign your AI with internal expectations and professional norms:
1. Rebalance Your Fine-Tuning Datasets
If you fine-tune your own model, prioritize internal documentation, reports, support tickets, and customer emails. Limit reliance on emoji-heavy external sources (if you don’t fine-tune models, the same can be said for your RAG data).
2. Standardize Prompt Tone with Emojis as Structure Cues
Sometimes emojis help, and if you are using a chatbot like Gemini or ChatGPT, use emojis tactically in prompt templates (e.g., ✅ for action, ❌ for blockers, 🧠 for insight). I use emojis for my prompt templates when I am meta prompting because it helps me visually break up the flow of my prompts and makes them easier to read and edit.
3. Build Emoji Guardrails in Prompt Policies
Define acceptable emoji use by function or user group. This means you either are using ChatGPT’s custom instructions or the actual system prompts in your own model. Legal memos ≠ launch campaign decks.
Common Missteps
Overgeneralizing: Assuming emojis are universally understood. They’re not.
Under-controlling: Allowing training on team chats without formatting or tone filtering.
Ignoring Output Tone Drift: As models continue interacting, emoji behavior can amplify without oversight.
If Pictures are worth 1,000 words, How Many are Emojis Worth?
When used well, emoji-aware prompting creates faster comprehension, clearer structure, and better sentiment alignment. But unchecked, it can lead to brand voice drift and output that feels unprofessional.
This week I focused on some tools that help optimize prompting, memory, and agent building.
PromptPerfect – Unlock the power of models like GPT-4, Claude and Midjourney. Optimize prompts in seconds. Optimize prompts for tone and clarity with side-by-side emoji vs. plain comparisons.
MemSync - Unified Memory for all of your AI apps. One-click to personalize and optimize your prompt on any AI platform to get the best responses. MemSync does this by aggregating your data, learning who you are, and creating a personalized memory vault that follows you across ChatGPT, Claude, Grok, and beyond.
Prompt of the Week: Study and Learn
“Study and learn” is an intelligent tutoring mode inside ChatGPT that helps you deeply understand material—not just get answers. It slows down response pacing, checks your understanding with Socratic questions, breaks concepts into manageable chunks, and helps reinforce knowledge with quizzes, guided practice, and reflection prompts. It remembers your inputs during the session and adapts to your learning level.
This tool works across subjects—from programming to business strategy—and helps professionals build durable, applied knowledge.
Though when it comes to cutting-edge AI topics, it’s not so hot, which is ironic considering that they are the leader in AI. 🤣 🤖
How to Use It
Open ChatGPT and click on the Tools dropdown (right above the input box).
Select “Study and learn”.
Paste in the markdown prompt below or start a conversation based on your topic.
Tip: You can also type /study and learn into the prompt box to trigger it faster.
Prompt to Start a “Study and Learn” Session
I’d like to begin a Study and Learn session.
Topic: **[Insert Your Topic Here]**
Please take the role of a tutor. Start by asking me a few questions to assess what I already know. Based on my answers, tailor the depth and pace of the material.
Present the topic in clear, teachable segments. After each segment, check my understanding with short quizzes, open-ended questions, or mini-exercises.
If I’m struggling, guide me with hints rather than revealing the full answer immediately.
Ask reflection questions throughout like:
- “What’s one way you could apply this?”
- “What part of this explanation was unclear?”
If I upload or paste a document, extract the key ideas, explain them, and create a learning structure around that material. Help me turn passive reading into active mastery.
At the end, summarize the key points and suggest what I should study next.
Let me know when you're ready to begin.
Optional: Enable Memory for Future Recall
To make “learned” content persist across sessions:
Go to Settings → Personalization → Memory.
Make sure memory is enabled.
You can say: “Remember this framework so we can build on it later”.
Also, you should see the “Updating Memory” message when you do that.
I wrote this in Notion, then as I typically do I cut and paste it into ChatGPT to verify and help me improve. Ironically, it added the emojis and I thought given the topic of this edition I’d just leave them in there.
Give the “Study and Learn” mode a try this week and see how it changes the way you pick up new material. Let us know if you’ve tested it out — we’d love to hear how it worked for you.
I appreciate your support.
Your AI Sherpa,
Mark R. Hinkle
Publisher, The AIE Network
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