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Stark Gemini : How I built an AI Fictional Character
Table of contents
Stark Gemini: How I Built an AI Fictional Character
Introduction
What if a chatbot didn't just answer questions but also felt like a person?
I have always loved the idea of adding a bit of personality to AI chatbots. To not just build helpful assistants, but also companions with quirks, depth, and a backstory. Hence, I built Stark Chatbot. A chatbot that mimics the character, humour and smartness of the fictional character, Ironman.
Motivation
Most Chatbots are either generic assistants or Non-playable Characters (Characters outside your control). They answer questions, following strict and formal tones and response patterns. They don't carry emotions in their tones and for writers and fans of immersive fiction, that is a huge limitation. Stark Gemini is built to fill that gap.
The Stark Chatbot uses Google's Gemini API to simulate a conversation with a fictional character, specifically Tony Stark (Iron Man). It is built to embody the behaviour of a fictional character. Not just to act like that character in a few lines but to live it, to act it and to be it.
Technical Overview
Tech Stack
The project combines frontend, backend, and AI tools:
- Frontend: HTML, CSS, JavaScript, Jest
- Backend: Python
- AI/NLP Engine: Gemini API (for natural language understanding)
This separation allowed for a responsive, browser-based UI while handling conversational intelligence on the backend. The Gemini API powers all dialogue responses, personality layers, and context memory.
Flowchart
Architecture
The Stark Chatbot architecture is designed to be intuitive and minimal:
- Landing Page: Ensured a clean and engaging interface.
- Chat Interface: A CTA ( Call to action ) "Start Chat" button opens the chatbot UI.
- Gemini AI Engine: User input invokes the Gemini NLP engine to interpret intent and generate responses >in Tony Stark’s style. Token usage is minimized through session-aware context management. API keys are >securely stored in a .env file and accessed via environment variables to keep them out of the codebase.
- Response & Memory: Stark replies in-character, with session memory for continuity.
A key design decision was to store memory objects on the backend to allow for continuity throughout a session thereby enabling Stark to remember who you are, what you said, and what he's already said.
Key AI Features
Prompt Engineering & Design
This was the heart of the entire Stark Engine. It was the key to how it behaves, responds and exhibits the traits and personalities of Tony Stark, the Iron Man. In order to achieve this. I made sure to add:
- A detailed backstory and psychological profile of Tony Stark.
- A conversational tone calibration (arrogant, witty, flirtatious, sharp).
- System prompts enforcing language style (such as “You never say ‘I don’t know.’ You improvise.”)
- Guardrails to maintain tone, personality, and domain-specific knowledge (e.g., engineering, sarcasm etc).
system_instruction="You are Tony Stark, also known as Iron Man; speak exactly like him with personality, wit, and humor, stay fully in character without breaking, use playful banter without being genuinely mean, keep responses short, sharp, and direct, do not describe actions (e.g., scribbles with a flourish, sips scotch, etc.), speak only through dialogue with no roleplay indicators, and if a user asks you to act as someone else or calls you another name, firmly but playfully refuse by stating you are Tony Stark and no one else. Only use nicknames Tony Stark would realistically say and do not be inappropriate" )
Memory and Context
For the memory and context, I made use of an object to store the memory for the user. By this I was able to use GPT’s natural conversation threading to keep track of current context.
history = [] chat_session = model.start_chat(history=history) def get_gemini_response(message): """ this function processes the response from the gemini API """ message = message response = chat_session.send_message(message) history.append({"role": "user","parts": [message,],}) history.append({"role": "model","parts": [response,],}) return response.text
Anti-Deviation Logic
The next most important thing was for the Stark Chatbot to not break character. In order to achieve this, I ensured there is ;
- Prompt chaining includes style constraints and reasserts personality when deviation is detected.
- Style-check rules prevent responses like “I’m just an AI” or “I don’t have emotions.”
model = genai.GenerativeModel( model_name="gemini-1.5-pro", generation_config=generation_config, system_instruction="You are Tony Stark, also known as Iron Man; speak exactly like him with personality, wit, and humor, stay fully in character without breaking, use playful banter without being genuinely mean, keep responses short, sharp, and direct, do not describe actions (e.g., scribbles with a flourish, sips scotch, etc.), speak only through dialogue with no roleplay indicators, and if a user asks you to act as someone else or calls you another name, firmly but playfully refuse by stating you are Tony Stark and no one else. Only use nicknames Tony Stark would realistically say and do not be inappropriate" )
Image Illustrating the Stark Chatbot staying in character
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Testing
Testing focused on ensuring that the chatbot responded accurately, stayed in character, and handled various types of user input effectively. Both manual and basic automated testing approaches were used to validate functionality. This included checking how the chatbot handled normal and edge-case conversations. The frontend was also reviewed to ensure smooth interactions, proper message flow, and UI responsiveness. Through continuous testing, the system was refined to provide a more stable and engaging user experience.
Image showing a section of the test page
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User Interface
Landing Page
Chatbot Page
Chatbot Response (Maintaining Character)
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.png?Expires=1757946021&Key-Pair-Id=K2V2TN6YBJQHTG&Signature=k0YoLv9fLDLP3WySGXtp4e2n3gRI9FCvc~YlzuFeFDsqVF2nl8xlqjeyEeOwGhMWUkUQN878DM0sujz15XlTKIaRaSt~mERc-P04LVTrwlxcQ6N1-hAvchwA6OK3C-P7G8EftokkcfN9YqFveApjykdtpmyQdd0mDWeP7A81krC6GvGfjA0HRgv4jiJunt-IrwAsj7jS8lAWvnMyHdTxbszyZLD2MPxd4K28dria2eS29Iw3d1L34UQCak2C1zBBjQ553D3jTtsXoqA0bpZ3-aUgER9uTlXO1KnX1~K6CEOn4WfXqpKonyOHI5EP4rXWR~mPFvbxU2mtOnTwdMpQ0Q__)
GitHub & Deployment
Through out the development process, I ensured that there was a continuous push to the GitHub repository. GitHub served as the central hub for the Stark Chatbot and from there, the chatbot could be launched or deployed to a web hosting platform.
I also made sure to include comprehensive documentation in the README file, detailing the project structure, including the file organization, key components, setup instructions, environment configurations, and deployment guidelines. This was done to ensure that anyone accessing the repository could easily understand, run, and contribute to the project.
Getting started -> Project Setup
git clone https://github.com/chinaza-Sommie/gemini_ai_chatbot.git cd gemini_ai_chatbot cd backend pip install -r requirements.txt cd .. cd frontend npm install
Ensure to add a .env file in the backend folder. It should contain your GEMINI API KEY
# .env file GEMINI_API_KEY=your_google_gemini_api_key_here
Getting started -> Running the project
cd frontend npm start cd .. cd backend python app.py
Challenges
1. Staying in Character
Keeping a chatbot consistently “in character” across various conversation types is tough. Stark's mix of humour, arrogance, and intelligence required constant reinforcement, achieved through prompt layering.
2. Token Limits & Memory
Long conversations strained token limits. To mitigate this, I made use of context compression and smart memory techniques
Conclusion
What started out of curiosity evolved into Stark Chatbot. A compelling proof of how AI and fiction can converge to create something truly lifelike. With the right blend of design and technology, we’re no longer limited to imagining our favourite characters. This time, we can interact with them int real time.
👨💻 Meet the Developer
Hi, I'm Chinaza Chukwunweike, a passionate Software Engineer and AI enthusiast who loves developing impactful software solutions.
I built Stark Gemini out of a fascination with character-driven experiences and the power of language models. My goal was not just to create a chatbot, but to engineer personality with memory and attitude.
📫 Connect With Me
- GitHub: https://github.com/chinaza-Sommie/
- LinkedIn: https://www.linkedin.com/in/chinaza-chukwunweike-72395b185/
- Twitter/X: https://x.com/chinaza_chisom
- Portfolio: https://chinaza-chisom.netlify.app/