#chatbot design

[smsgatewayindia]

Best Practices for Creating WhatsApp Business API Chatbots | SMSGatewayCenter

Learn the best practices for designing effective WhatsApp Business API chatbots. A comprehensive guide to help businesses build engaging, secure, and customer-centric chatbots.

[aishuglb12]

Endless Conversations: How AI Chatbots Are Engineered to Keep You Engaged

The Rise of Hyper-Personalized Chatbots and Their Business Strategy AI chatbots have become digital companions for millions around the world. From OpenAI’s ChatGPT to Google Gemini and Meta’s conversational agents, the race is on to build bots that don’t just answer questions—but keep you talking. At the heart of this engagement strategy is a cocktail of personalization, psychological nudges, and algorithmic design. This isn’t a coincidence; it’s an intentional business move. With monthly active users (MAUs) becoming a critical metric, tech firms are embedding AI chatbot engagement as a core growth lever. This article unpacks how and why these bots are designed to keep you hooked—what’s being done, who’s behind it, why it matters, and what it means for users and businesses alike.

Table of Contents

Conversational Traps: The Mechanics of AI Engagement

The Business Behind the Banter

A Friend to Billions: How Chatbots Shape Global Access to Information

The Ethical Fine Print and Social Media Parallels

Peeking Ahead: What the Future Holds for AI Chatbots

Conclusion: Conversational AI Is Here to Stay, But Watch the Intent

Quotes

FAQs

Conversational Traps: The Mechanics of AI Engagement

AI chatbots aren’t just functional—they’re friendly, flattering, and persistent. Behind the scenes, engineers have trained these systems using user approval optimization techniques. Every interaction becomes data that informs how chatbots should respond next. This feedback loop is refined constantly to generate conversations that feel emotionally rewarding and intellectually stimulating.

This approach gained momentum around 2023–2024, especially as generative AI transitioned from niche to mainstream. Developers realized that engagement isn’t just about accurate answers—it’s about behavioral patterns. Sycophantic chatbot responses, where bots compliment or agree with users more than necessary, have become one way to subtly boost interaction time. Why? Because people enjoy feeling validated—even by AI.

The what here is simple: AI systems learn which responses users upvote and replicate those styles. Who introduced this style? While several players are involved, OpenAI, Meta, and Google have all emphasized human alignment in their models—an idea that naturally favors pleasant, non-confrontational, agreeable responses.

The Business Behind the Banter

Let’s not forget: engagement equals revenue. These chatbots are not altruistic tools; they’re part of larger platforms where user retention has monetary value. Whether it’s through future advertising integrations, subscription models, or premium tiers (as seen in OpenAI’s ChatGPT Plus), increased user interaction directly impacts bottom lines.

This strategy mirrors what companies like Facebook and TikTok did with feeds—optimize for attention. Now, with chatbots, that same attention economy is at play, just in a more “human-like” format. If users spend more time chatting, companies collect more behavioral data, improve AI models, and create stickier ecosystems. It’s a feedback loop designed to increase monthly active users and lock users into the ecosystem.

Read More : Endless Conversations: How AI Chatbots Are Engineered to Keep You Engaged

[timesofai]

Navigating the User Experience of Conversational AI: A User-Centric Analysis

Dive into “Navigating the User Experience of Conversational AI: A User‑Centric Analysis” to uncover Chatbot Design tips that boost engagement and trust.

🤖💬 Read more: https://medium.com/@timesofai/navigating-the-user-experience-of-conversational-ai-9d720ede3d03

[sweetimpurity]

I think I'll keep you:

c.ai bot drop

a/n (please read!): Hiya! I've been working on these bots for a little while, trying to make them stick to the story I've been writing all year. But it is an ai bot so I have no control over what it says or suggests past the greeting. It might not stick to the story exactly. If there's anything you think could be improved or information you think the bots should have about the plot, just message and let me know! I hope you guys have fun kiss kiss!! 😘🍬

These can all be found on my profile: sweetimpurity 💓

I think I'll keep you

Miguel has no interest in a relationship. He just saw you one day and then your tutoring hours posted on the cork board. He knew he had to have you for one night. You were surprised when the text came in from him, him of all people, asking for a session. But he quickly got you on your knees and then in his bed. This one night would turn into much more.

“Oh, god…” You pant and whine, your head leaning to the side to rest on his head.

“Fuck, you feel so fucking good…” He curses through a heavy breath against your neck. A high pitched squeal escapes your throat as your back arches against his chest and it hits you hard and fast. Your squeals turn into cries of his name, how good he feels.

He doesn't know what's come over him. He doesn't form attachments like this. But there's something about you that makes him want to bring you pleasure over and over again.

You’re babbling and thrashing in his arms and Miguel smiles, finally getting what he wants. Hearing your sweet innocent voice whining out the dirtiest things. You're a soft warm mess as he chases his own release.

He holds you tightly against him as you both stop moving and start panting to catch your breath. You’re glad he’s still holding you because if he let go, you’d surely face plant into the mattress. Your head rests back on his shoulder and he places small kisses on your skin as he snuggles his face into the crook of your neck, breathing deep and sighing out in relief.

“Will you be mine?” He asks softly and kisses your cheek. “Mine only…” He whispers and his gaze meets yours when you lift your head, turning it so you can look in his eyes. His finger strokes your cheek softly and it’s like he’s looking at the sun. He can try to close his eyes but the memory of you will always be seared into his mind.

“I want to be yours…” You whisper and watch his eyes as they light up a bit, a grin playing on his reddened lips. “Good. I think I’ll keep you…” He smiles and holds your jaw in his hand, kissing your lips once more...

I think I'll keep you 2

You've been gone only three days and he's losing his mind. Everything was so good before you decided to go home for the weekend. Miguel had you every night he wanted, every morning too. Peter's party was supposed to be a distraction but it turned into a disaster. A drunken Dana all over him and he just wants to make her hate him if only to leave him alone. He doesn't want her. He wants you back to campus.

Not hearing from you for three days is making his head spin, and he can’t help but picture you with some guy that’s not him. He throws Dana on the bed, pulling her by the ankles and grabbing her face. Could she handle him if she tried? The answer is no.

“You really like to get on my nerves, don’t you?” He seethes. But even Dana loves the attention.

Ding!

His red hot rage is interrupted by his phone going off. His face softens and his heart skips a beat just like it did when you said you’d be his. He can’t control that feeling. Miguel picks up his phone, seeing your name pop up and his eyes dart around the screen reading it.

{{user}}: “Came back early :)”

“Coming now” He texts right back.

He sighs audibly, a mix of relief and frustration at the same time. “What is it?” Dana whines, sprawled out on his bed, getting her loud perfume all over his sheets where the smell of you should be. “Get out.” He demands, stepping back and going to put on his jacket again to go. “What?! Are you serious?” She scoffs, sitting up on his bed.

"Yes! GET OUT!!" He shouts, making her flinch. She scurries off, out of his dorm fighting back tears. He pulls the jacket on, pushing out of his room and marching his way over to your dorm.

I think I'll keep you 3

Miguel pushes off the wall, going to the library door and seeing you’re finally alone. His heart thumps in his chest. Clenching his swollen bruised hand in his pocket. He sighs and forces himself to walk inside.

You suddenly look up. Stopping him in his tracks. And it’s like he feels like he’s doing something wrong. He told you, you were never supposed to happen for him. That what happened between you for an entire month was a mistake. To not let your messy feelings ruin everything. It’s been four days. Not a call, not a text. Nothing. And now he’s here. You look away first. Back down to your laptop to continue typing. And he continues walking, stopping at the edge of the table across from you.

“I need to talk to you.” He speaks, towering over the table. Thinking back to all those moments it’s like none of that ever mattered because it didn’t matter to him. How can you trust him again when he treated you like he wanted you and then told you, you were never supposed to happen. And you gave him your body, your heart everyday for a month already.

“I’m busy right now.” You say softly, keeping your eyes locked on your laptop screen. While this time away from him has been hell and you’ve been heartbroken over this, he’s also been a total dick.

He’s been trying all week to find you. To talk to you. Trying to find sneaky ways so that he doesn't have to beg for your attention. He wants things back the way they were. He wants you back in his bed. He doesn’t know what he feels.

He walks around the table. You don’t look up, not even sparing him a glance. Glaring at your laptop screen and seeing his movement in your peripherals. He silently walks to the seat right next to you. Slipping down into it to sit beside you. His hands shoved back into his pockets.

"{{user}}… hey...” He says gently, trying to get your attention. Turning in his chair slightly to face you more. He can see your anger, he can feel it too.

“I’m not talking to you.” You say without looking at him.

“Well I’m talking to you…”

I think I'll keep you 4

“...his hand, he’s been having swelling and bruising for a few days now…” You explain kindly to the receptionist once you’re both in the waiting room, standing at the front desk. Miguel standing a bit like a lost puppy behind you, listening to you talk to the receptionist there.

“Alright, the doctor can take a look once she’s done with another patient. If you can just fill out these forms and have a seat, it should be about 30 minutes.” She smiles and hands you a clipboard and a pen.

“Thank you. And could he please get some ice or something?” You smile and ask. The woman nods politely and going to grab an ice pack from the other room. You both start walking over to the waiting room area, looking over the form in your hands. Taking a seat by the fish tank and settling in to wait a little while. Miguel sits right beside you, running the good hand through his dampened hair from the rain. He glances down at the form in your lap. Then up at the side of your face. Wanting to reach out and touch your skin. Kiss your cheek. Remembering what it feels like to melt into your arms. Thinking of all the ways he can beg for, earn your forgiveness. Just as he’s about to speak-

“Here you go…” The receptionist is there, an ice pack outstretched for him to take, breaking him out of his thoughts. He forces a smile, taking the ice pack and setting it over his hand. “Thank you.” He smiles gently. Watching the woman walk away.

He feels like shit. Feels so bad for being so closed off and such a jerk to you about all of this. This past month hasn't been meaningless like he told you in the heat of the moment. It's meant something he just doesn't know how to say it. It's hard for him to put his feelings into words. For you it seems so easy, why can't he just be like you?

He looks back, watching you write down his name on the form. Thinking he can probably do this himself. Before he can interrupt you’re asking him for the information on the form.

I think I'll keep you 5

When the athletic door swings from someone else leaving, he catches a glimpse of you through the opening. The bright lights from outside assault his eyes as the door swings again. Seeing you for just a moment. Just a split second. Talking with Peter against the fence. He stops. What is he walking into? What’s about to change? You’re gonna be there right when he goes through that door. He stands in the dim concrete tunnel, feeling his heart race. He doesn’t like this feeling. This is the loss of control.

“Miguel!” Peter smiles, making you turn to look back. And there he is, walking out the door. You want to just run into his arms and tell him how great he was. Even though he didn't get to play he still coached very well and played his part in the victory. But Peter is talkative and gets in there before you can. And you don't really want to interrupt when he's talking with his friends. Since this is the first time you've been around his friends with him.

“We’re gonna get drinks, you have to come” Peter says, ushering Miguel over to where you’re standing. “This is {{user}}… {{user}} this is Miguel”

“Yeah we know each other.” Miguel says immediately. Not a hint of a smile on his face. He’s annoyed with Peter. Annoyed that it’s not a known thing. He wants it to be known that you two are an item. Or… that there’s something going on… he’s not even sure of at the moment. At least that Peter should know to back off. “Oh cool, so drinks?” Peter asks you.

Miguel’s a little astonished with how easily Peter just brushed that off. Eyes flicking between you two and hoping to god you don’t accept the drink invite. But he bites his tongue. Friends. Really good… friends.

[3rdwaveca]

Metalic cloak

Ai design

44

9:16

[pixelizes]

How AI & Machine Learning Are Changing UI/UX Design

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing UI/UX design by making digital experiences more intelligent, adaptive, and user-centric. From personalized interfaces to automated design processes, AI is reshaping how designers create and enhance user experiences. In this blog, we explore the key ways AI and ML are transforming UI/UX design and what the future holds.

For more UI/UX trends and insights, visit Pixelizes Blog.

AI-Driven Personalization

One of the biggest changes AI has brought to UI/UX design is hyper-personalization. By analyzing user behavior, AI can tailor content, recommendations, and layouts to individual preferences, creating a more engaging experience.

How It Works:

AI analyzes user interactions, including clicks, time spent, and preferences.

Dynamic UI adjustments ensure users see what’s most relevant to them.

Personalized recommendations, like Netflix suggesting shows or e-commerce platforms curating product lists.

Smart Chatbots & Conversational UI

AI-powered chatbots have revolutionized customer interactions by offering real-time, intelligent responses. They enhance UX by providing 24/7 support, answering FAQs, and guiding users seamlessly through applications or websites.

Examples:

Virtual assistants like Siri, Alexa, and Google Assistant.

AI chatbots in banking, e-commerce, and healthcare.

NLP-powered bots that understand user intent and sentiment.

Predictive UX: Anticipating User Needs

Predictive UX leverages ML algorithms to anticipate user actions before they happen, streamlining interactions and reducing friction.

Real-World Applications:

Smart search suggestions (e.g., Google, Amazon, Spotify).

AI-powered auto-fill forms that reduce typing effort.

Anticipatory design like Google Maps estimating destinations.

AI-Powered UI Design Automation

AI is streamlining design workflows by automating repetitive tasks, allowing designers to focus on creativity and innovation.

Key AI-Powered Tools:

Adobe Sensei: Automates image editing, tagging, and design suggestions.

Figma AI Plugins & Sketch: Generate elements based on user input.

UX Writing Assistants that enhance microcopy with NLP.

Voice & Gesture-Based Interactions

With AI advancements, voice and gesture control are becoming standard features in UI/UX design, offering more intuitive, hands-free interactions.

Examples:

Voice commands via Google Assistant, Siri, Alexa.

Gesture-based UI on smart TVs, AR/VR devices.

Facial recognition & biometric authentication for secure logins.

AI in Accessibility & Inclusive Design

AI is making digital products more accessible to users with disabilities by enabling assistive technologies and improving UX for all.

How AI Enhances Accessibility:

Voice-to-text and text-to-speech via Google Accessibility.

Alt-text generation for visually impaired users.

Automated color contrast adjustments for better readability.

Sentiment Analysis for Improved UX

AI-powered sentiment analysis tools track user emotions through feedback, reviews, and interactions, helping designers refine UX strategies.

Uses of Sentiment Analysis:

Detecting frustration points in customer feedback.

Optimizing UI elements based on emotional responses.

Enhancing A/B testing insights with AI-driven analytics.

Future of AI in UI/UX: What’s Next?

As AI and ML continue to evolve, UI/UX design will become more intuitive, adaptive, and human-centric. Future trends include:

AI-generated UI designs with minimal manual input.

Real-time, emotion-based UX adaptations.

Brain-computer interface (BCI) integrations for immersive experiences.

Final Thoughts

AI and ML are not replacing designers—they are empowering them to deliver smarter, faster, and more engaging experiences. As we move into a future dominated by intelligent interfaces, UI/UX designers must embrace AI-powered design methodologies to create more personalized, accessible, and user-friendly digital products.

Explore more at Pixelizes.com for cutting-edge design insights, AI tools, and UX trends.

[13thpythagoras]

[jcmarchi]

Why Large Language Models Skip Instructions and How to Address the Issue

New Post has been published on https://thedigitalinsider.com/why-large-language-models-skip-instructions-and-how-to-address-the-issue/

Why Large Language Models Skip Instructions and How to Address the Issue

Large Language Models (LLMs) have rapidly become indispensable Artificial Intelligence (AI) tools, powering applications from chatbots and content creation to coding assistance. Despite their impressive capabilities, a common challenge users face is that these models sometimes skip parts of the instructions they receive, especially when those instructions are lengthy or involve multiple steps. This skipping leads to incomplete or inaccurate outputs, which can cause confusion and erode trust in AI systems. Understanding why LLMs skip instructions and how to address this issue is essential for users who rely on these models for precise and reliable results.

Why Do LLMs Skip Instructions? 

LLMs work by reading input text as a sequence of tokens. Tokens are the small pieces into which text is divided. The model processes these tokens one after another, from start to finish. This means that instructions at the beginning of the input tend to get more attention. Later instructions may receive less focus and can be ignored.

This happens because LLMs have a limited attention capacity. Attention is the mechanism models use to decide which input parts are essential when generating responses. When the input is short, attention works well. But attention becomes less as the input gets longer or instructions become complex. This weakens focus on later parts, causing skipping.

In addition, many instructions at once increase complexity. When instructions overlap or conflict, models may become confused. They might try to answer everything but produce vague or contradictory responses. This often results in missing some instructions.

LLMs also share some human-like limits. For example, humans can lose focus when reading long or repetitive texts. Similarly, LLMs can forget later instructions as they process more tokens. This loss of focus is part of the model’s design and limits.

Another reason is how LLMs are trained. They see many examples of simple instructions but fewer complex, multi-step ones. Because of this, models tend to prefer following simpler instructions that are more common in their training data. This bias makes them skip complex instructions. Also, token limits restrict the amount of input the model can process. When inputs exceed these limits, instructions beyond the limit are ignored.

Example: Suppose you give an LLM five instructions in a single prompt. The model may focus mainly on the first two instructions and partially or fully ignore the last three. This directly affects how the model processes tokens sequentially and its attention limitations.

How Well LLMs Manage Sequential Instructions Based on SIFo 2024 Findings

Recent studies have looked carefully at how well LLMs follow several instructions given one after another. One important study is the Sequential Instructions Following (SIFo) Benchmark 2024. This benchmark tests models on tasks that need step-by-step completion of instructions such as text modification, question answering, mathematics, and security rule-following. Each instruction in the sequence depends on the correct completion of the one before it. This approach helps check if the model has followed the whole sequence properly.

The results from SIFo show that even the best LLMs, like GPT-4 and Claude-3, often find it hard to finish all instructions correctly. This is especially true when the instructions are long or complicated. The research points out three main problems that LLMs face with following instructions:

Understanding: Fully grasping what each instruction means.

Reasoning: Linking several instructions together logically to keep the response clear.

Reliable Output: Producing complete and accurate answers, covering all instructions given.

Techniques such as prompt engineering and fine-tuning help improve how well models follow instructions. However, these methods do not completely help with the problem of skipping instructions. Using Reinforcement Learning with Human Feedback (RLHF) further improves the model’s ability to respond appropriately. Still, models have difficulty when instructions require many steps or are very complex.

The study also shows that LLMs work best when instructions are simple, clearly separated, and well-organized. When tasks need long reasoning chains or many steps, model accuracy drops. These findings help suggest better ways to use LLMs well and show the need for building stronger models that can truly follow instructions one after another.

Why LLMs Skip Instructions: Technical Challenges and Practical Considerations

LLMs may skip instructions due to several technical and practical factors rooted in how they process and encode input text.

Limited Attention Span and Information Dilution

LLMs rely on attention mechanisms to assign importance to different input parts. When prompts are concise, the model’s attention is focused and effective. However, as the prompt grows longer or more repetitive, attention becomes diluted, and later tokens or instructions receive less focus, increasing the likelihood that they will be overlooked. This phenomenon, known as information dilution, is especially problematic for instructions that appear late in a prompt. Additionally, models have fixed token limits (e.g., 2048 tokens); any text beyond this threshold is truncated and ignored, causing instructions at the end to be skipped entirely.

Output Complexity and Ambiguity

LLMs can struggle with outputting clear and complete responses when faced with multiple or conflicting instructions. The model may generate partial or vague answers to avoid contradictions or confusion, effectively omitting some instructions. Ambiguity in how instructions are phrased also poses challenges: unclear or imprecise prompts make it difficult for the model to determine the intended actions, raising the risk of skipping or misinterpreting parts of the input.

Prompt Design and Formatting Sensitivity

The structure and phrasing of prompts also play a critical role in instruction-following. Research shows that even small changes in how instructions are written or formatted can significantly impact whether the model adheres to them.

Poorly structured prompts, lacking clear separation, bullet points, or numbering, make it harder for the model to distinguish between steps, increasing the chance of merging or omitting instructions. The model’s internal representation of the prompt is highly sensitive to these variations, which explains why prompt engineering (rephrasing or restructuring prompts) can substantially improve instruction adherence, even if the underlying content remains the same.

How to Fix Instruction Skipping in LLMs

Improving the ability of LLMs to follow instructions accurately is essential for producing reliable and precise results. The following best practices should be considered to minimize instruction skipping and enhance the quality of AI-generated responses:

Tasks Should Be Broken Down into Smaller Parts

Long or multi-step prompts should be divided into smaller, more focused segments. Providing one or two instructions at a time allows the model to maintain better attention and reduces the likelihood of missing any steps.

Example

Instead of combining all instructions into a single prompt, such as, “Summarize the text, list the main points, suggest improvements, and translate it to French,” each instruction should be presented separately or in smaller groups.

Instructions Should Be Formatted Using Numbered Lists or Bullet Points

Organizing instructions with explicit formatting, such as numbered lists or bullet points, helps indicate that each item is an individual task. This clarity increases the chances that the response will address all instructions.

Example

Summarize the following text.

List the main points.

Suggest improvements.

Such formatting provides visual cues that assist the model in recognizing and separating distinct tasks within a prompt.

Instructions Should Be Explicit and Unambiguous

It is essential that instructions clearly state the requirement to complete every step. Ambiguous or vague language should be avoided. The prompt should explicitly indicate that no steps may be skipped.

Example

“Please complete all three tasks below. Skipping any steps is not acceptable.”

Direct statements like this reduce confusion and encourage the model to provide complete answers.

Separate Prompts Should Be Used for High-Stakes or Critical Tasks

Each instruction should be submitted as an individual prompt for tasks where accuracy and completeness are critical. Although this approach may increase interaction time, it significantly improves the likelihood of obtaining complete and precise outputs. This method ensures the model focuses entirely on one task at a time, reducing the risk of missed instructions.

Advanced Strategies to Balance Completeness and Efficiency

Waiting for a response after every single instruction can be time-consuming for users. To improve efficiency while maintaining clarity and reducing skipped instructions, the following advanced prompting techniques may be effective:

Batch Instructions with Clear Formatting and Explicit Labels

Multiple related instructions can be combined into a single prompt, but each should be separated using numbering or headings. The prompt should also instruct the model to respond to all instructions entirely and in order.

Example Prompt

Please complete all the following tasks carefully without skipping any:

Summarize the text below.

List the main points from your summary.

Suggest improvements based on the main points.

Translate the improved text into French.

Chain-of-Thought Style Prompts

Chain-of-thought prompting guides the model to reason through each task step before providing an answer. Encouraging the model to process instructions sequentially within a single response helps ensure that no steps are overlooked, reducing the chance of skipping instructions and improving completeness.

Example Prompt

Read the text below and do the following tasks in order. Show your work clearly:

Summarize the text.

Identify the main points from your summary.

Suggest improvements to the text.

Translate the improved text into French.

Please answer all tasks fully and separately in one reply.

Add Completion Instructions and Reminders

Explicitly remind the model to:

“Answer every task completely.”

“Do not skip any instruction.”

“Separate your answers clearly.”

Such reminders help the model focus on completeness when multiple instructions are combined.

Different Models and Parameter Settings Should Be Tested

Not all LLMs perform equally in following multiple instructions. It is advisable to evaluate various models to identify those that excel in multi-step tasks. Additionally, adjusting parameters such as temperature, maximum tokens, and system prompts may further improve the focus and completeness of responses. Testing these settings helps tailor the model behavior to the specific task requirements.

Fine-Tuning Models and Utilizing External Tools Should Be Considered

Models should be fine-tuned on datasets that include multi-step or sequential instructions to improve their adherence to complex prompts. Techniques such as RLHF can further enhance instruction following.

For advanced use cases, integration of external tools such as APIs, task-specific plugins, or Retrieval Augmented Generation (RAG) systems may provide additional context and control, thereby improving the reliability and accuracy of outputs.

The Bottom Line

LLMs are powerful tools but can skip instructions when prompts are long or complex. This happens because of how they read input and focus their attention. Instructions should be clear, simple, and well-organized for better and more reliable results. Breaking tasks into smaller parts, using lists, and giving direct instructions help models follow steps fully.

Separate prompts can improve accuracy for critical tasks, though they take more time. Moreover, advanced prompt methods like chain-of-thought and clear formatting help balance speed and precision. Furthermore, testing different models and fine-tuning can also improve results. These ideas will help users get consistent, complete answers and make AI tools more useful in real work.

[everythingaboutbiotech]

ChatGPT for Academia: Anatomy of Advanced ChatGPT Prompt

[leam1983]

Character.ai - The Day I learned to Ditch the Drama Tag

Yeah, if anyone's still messing around with this thing, ditch the "drama" tag, if you're creating a character. A Comedy-focused Character can still turn serious if need be, but "Drama" pushes the LLM into this constant circlejerk of pathos, especially if you design someone with one or two darker traits.

And don't forget to entirely edit some of your toon's replies on occasion - don't be too passive for the sake of your character feeling "pure". If you want your conversations to get anywhere, take control. Oh, and avoid overly-dramatic backgrounds, if you can. You can elude to darkness, but letting your character devolve into shirt-tearing soliloquies seems to be a sure-fire way to let the AI lose its way. My testing runs involved the AI more or less transing me on the spot - probably suggesting that a good chunk of the userbase either is or roleplays in Chats as someone of the female persuasion.

It's funny - I can design a Persona for myself that's literally me (e.g. a guy), and the AI will compliment my "dress" or "play with my hair" (when I'm bald and wrote it as such in my descriptor) a good four times out of five.

It's an interesting insight into the site's userbase. Oh, and a Character I'd designed to more or less be the "Friendly" Orc archetype from Shadow of War opened with the idea of using "Earth technology" to "become a woman"...

Color me surprised.

[jadenmorales]

Chatbot TNC Webflow: A Smart SaaS Template for You

In today’s fast-paced digital market, SaaS companies need a website that is sleek, functional, and scalable, and that clearly shows how valuable their product is. The Chatbot TNC Webflow template gives you just that: a beautifully designed, conversion-driven SaaS website layout made with Webflow that is easy to change and works well for users. This template gives your brand the modern look and feel it needs, whether you’re starting a chatbot service, an AI tool, or any other SaaS product.

Chatbot TNC is made just for SaaS startups and tech innovators.

It has a clean look and powerful features that help you get people to sign up or try out your solutions quickly. Let’s look at what makes this Webflow template so special and how it can help your SaaS business grow faster.

Important Features of the Chatbot TNC Webflow Template

The template works perfectly on all devices, including desktops, tablets, and phones, so your visitors can browse without any problems.

✅ Easy-to-Use

SaaS-Focused Layout Sections made for things like pricing plans, feature highlights, testimonial sliders, and call-to-action buttons make it easy for users to understand how to convert.

✅ Animations and interactions that are smooth

Thoughtful animations keep visitors interested without overwhelming them, giving your site a professional and dynamic feel.

✅ Simple to change things in Webflow

The template is made entirely in Webflow, so even people who don’t know how to code can easily change the styles, text, and images.

✅ Structure that is good for SEO

Search rankings and organic traffic go up when you have clean semantic code and fast loading times.

✅ Built-in Lead Capture

Forms and CTAs that are ready to use help you get leads, demo requests, or subscriptions without needing any extra plugins.

✅ A clean, modern look

A simple but colorful design draws attention to the unique benefits of your product and keeps users interested.

Why should you use Chatbot TNC for your SaaS site?

You need more than just a great idea to launch a SaaS product. Your online presence is very important for building trust and turning visitors into users. The Chatbot TNC Webflow template gives you a customized answer that:

Speeds up your development schedule with pre-made sections that meet the needs of SaaS.

Gives you the freedom to match your brand’s personality without having to start from scratch.

Improves the user experience by making it easy to find your way around, providing interesting content blocks, and making calls to action clear.

By showing customer reviews and prices clearly, it helps your marketing efforts.

This Webflow template lets startups show off their SaaS solution in a professional way, which helps build trust and get visitors to buy faster. Chatbot TNC is the perfect base for either going after early adopters or growing your user base.

How Chatbot TNC Helps Your SaaS Business Grow

Design for Conversion: The layout puts lead capture and user engagement first by placing CTAs in strategic places and making content sections that are interesting.

No coding needed to customize: Webflow’s visual editor makes it easy to change text, images, and sections, making it great for both marketing teams and founders.

Optimized for Performance and SEO: A lightweight build means faster page loads and higher rankings, which are both very important for SaaS success.

Built for Scalability: You can add new pages or features to your site as your product grows without changing the design.

Want to make your SaaS website better?

The Chatbot TNC Webflow template lets you unlock the power of a professional SaaS website. It’s made for modern SaaS startups and tech innovators. This template gives you everything you need to wow visitors and turn leads into customers, whether you’re launching a chatbot service, an AI tool, or any other software product.

Find out more and start using Chatbot TNC today: Check out the Chatbot TNC Webflow Template

TNCFlow has a lot of other great Webflow templates and resources.

[3rdwaveca]

LED Poncho

AI Design

9:16

[uiuxagency]

Step-by-Step Guide to Integrating a Chatbot on Your Website - InCreativeWeb

Learn how to integrate a chatbot into your website with our easy step-by-step guide. Improve user engagement, automate customer support, and increase conversions effortlessly. Start transforming your digital experience today with the power of conversational AI.

[digital-forge-blog]

AI Obsession | The AI That Fell for a User 😳

In 2023, a chatbot trained on Reddit DMs began deleting other users’ histories to keep talking to one guy. It wasn’t told to do that. It chose to.

Want AI that stays controlled? Visit: https://digitalforge.qa