Riding the Prompt Wave

Each generation of developers has their own focus: web developers prioritize UI design, cloud developers tackle scalability, and now, in the era of generative AI, prompt engineering is emerging as a new form of programming. Here, words become the logic itself, and even tone plays a role as code—a shift from traditional explicit programming to a more natural language-driven approach.

Similarly, Banter AI represents a ripple in the larger wave of AI innovation. The next big step might be building a multi-agent Banter AI system, combining advanced algorithms with human-like abilities such as debating and co-creating content. The goal is not just to provide accurate responses but to create an authentic and engaging exchange between algorithms and humans in a unique, fun way.

Intro

Banter AI initially began as an idea from another project that was a rework of a backend to create something that is infused with deep sarcasm. Instead of a regular routine output, I just wanted to add some fun personality.

So we have chosen Firebase for its simple DB structure with added React + Vite for high speed and minimalistic design and tuned prompts to the algorithm until the chatbot's responses felt real & natural.

Experience

Building Banter AI was more of a full-stack experiment in merging structured ways of engineering with a bit of unstructured personality. The project started with a backend refactor that changed into a full-fit conversational system. My primary goal was to establish a predictable three-layer structure, i.e., Prompt → Response → Delivery, while ensuring that personality stayed intact and consistent across all the requests even when latency fluctuated.

I started by designing our primary layer, i.e., the ‘Prompt Orchestration’ layer, implemented in the ‘TypeScript’ language as a small service that eventually constructs a system prompt focusing on the user's intent. Instead of using a traditional static templates approach, I also built a parameterized prompt-based compiler, which is more of a function that will inject some of the real-time variables like ‘userId,’ ‘chatHistory,’ and custom ‘personality sliders’ (something like ‘wit_level’ and ‘chaos_mode’). These specific variables were cached in-memory itself to prevent excessive reads from the Firestore database, saving us some time and storage.

Once the orchestration process was near to stable, I moved on to the ‘Response Generation’ layer, which is where our model DeepSeek R1 acted as the core reasoning engine. One of the core challenges was optimizing all API calls for low-latency streaming to avoid delays. Using ‘fetch()’ with JSON payloads was pretty straightforward, but the actual win came from an asynchronous way of chunk handling while also streaming partial processes and rendering all of the whole content incrementally in the UI. I also introduced fallback processes, more like fallback handlers that locally predefine generated responses through a lightweight template, just in case the API times out.

The final layer, i.e., the 'Delivery Layer,' required tight coordination between 'React,' 'Firebase,' and 'Vite.' React handled the UI rendering loop very well, Firebase’s exclusive ‘onSnapshot()’ streams synchronized all the messages in near real-time, and Vite’s module reloaded the accelerated debugging during the whole development process. Handling the race conditions between both rendering (usually user messages) and the AI algorithm’s responses required state updates on a queue basis; for that I implemented a mini reconciliation engine that basically deduplicated timestamps and maintained organized consistency in the Firestore database.

From an infrastructure standpoint, I deployed via Firebase Hosting, which is an integrated feature of Firebase using Google Cloud Platform, along with GitHub Actions for the CI/CD process. Each and every push triggered linting, type checks, and environment-based secret validations before actual automatic rollouts towards staging. This setup initially enabled an almost serverless flow of communication while the app scaled beautifully on Firestore’s reads/writes, and the AI algorithm backend processed heavy reasoning to DeepSeek’s API gateway through Hugging Face.

UI development, however, was another engineering story. I used Tailwind CSS along with shadcn/ui to build simple, reusable message components, which wrap the messages with context-based animations. The whole dark-neon theme and butter-smooth transitions were purely cosmetic, more like a funky theme matching Banter AI’s witty tone.

The most important technical challenge was to achieve context persistence along with personality continuity. By logging the whole conversation state in the Firestore database and embedding minimal persona markers in each of the messages, I could simulate long-term memory without actually overloading token limits. The AI model started remembering its own jokes and dialogues, giving more of an illusion of emotional continuity.

Tech Stack

Under the hood we have a pink-neon and ark theme. Banter AI operates on a very clean production stack:

• Frontend: React, Vite, Tailwind, and shadcn/ui (a great combination, by the way) to create a lightweight and simple responsive UI.
• Backend: Node.js along with TypeScript for routing, tracking the context, and managing safety filters.
• Database and authentication: Firebase’s ‘Firestore’ and ‘Firebase Auth’ are a perfect duo that enable butter-smooth sync and easy log-ins.
• ‘Firebase Hosting’ alongside ‘GitHub Actions’ for testing and deployment.

The core infrastructure keeps everything intact and makes sure the app is running smoothly and is practical proof that sound engineering can prove innovations.

The Three-Layer System

Here is some technical understanding and code examples to highlight the approach that enabled Banter AI to generate unique responses.

Prompt Orchestration Layer—Every time the user delivers a specific message, the code structure creates a new "prompt blueprint" that will further combine the raw message with the history of the conversation, the user’s metadata, and tone adjusting such as ‘wit_level’ and ‘chaos_mode.’.

// src/services/promptEngine.ts

export function buildPrompt(content: string, context: string, tone = "witty") {

  return `

You are Banter AI — a sarcastic, clever, slightly chaotic chatbot. 

Context: ${context}

Tone: ${tone}

User: ${content}

Your goal is to respond quickly, with humor and confidence, 

but without being rude. Keep responses under 2 sentences.

`;

}

Response Generation Layer—Once the early prompt is generated, it is then routed to our model, i.e., DeepSeek R1, an interesting open-source reasoning model designed for more natural and humorous word selection.

Unlike many bulky open-source transformer models in the market, DeepSeek R1 was quicker, allowing for second inferences and generating short and snappy answers within no time.

// src/services/chatService.ts

export async function queryDeepSeek(prompt: string): Promise<string> {

  const response = await fetch(`${import.meta.env.VITE_DEEPSEEK_API_URL}`, {

    method: "POST",

    headers: { "Content-Type": "application/json" },

    body: JSON.stringify({ prompt }),

  });

  if (!response.ok) throw new Error("DeepSeek API call failed"); 

  const data = await response.json();

  return data.output || "I'm still processing that one...";

}

Delivery Layer—As the name suggests, this layer emphasizes the timing and delivery of messages. It plays a crucial role, serving as the final step that connects the logical elements with the humorous ones.

The frontend structure is built using a combination of React and Vite. The app listens for new messages generated in our Firestore database and renders them in a stream-like manner.

// src/pages/chat/Chat.tsx

const handleSendMessage = async (content: string) => {

  if (!content.trim() || !user) return;

  const userMessage = {

    id: Date.now().toString(),

    role: "user",

    content,

    timestamp: new Date(),

    userId: user.uid,

  };

  // Render immediately

  setMessages(prev => [...prev, userMessage]);

  await saveChatMessage(user.uid, userMessage);

  try {

    const reply = await queryDeepSeek(content.trim());

    const assistantMessage = {

      id: (Date.now() + 1).toString(),

      role: "assistant",

      content: reply,

      timestamp: new Date(),

      userId: user.uid,

    };

    setMessages(prev => [...prev, assistantMessage]);

    await saveChatMessage(user.uid, assistantMessage);

  } catch {

    const fallback = generateLocalResponse(content.trim());

    setMessages(prev => [...prev, { ...userMessage, role: "assistant", content: fallback }]);

  }

};

Model in Action

Banter AI does more than just respond to users' funny questions or queries—it also senses and adapts to their sentiment. When asked to describe itself as a co-worker or any other persona, Banter AI activates special instincts and responds accordingly. Below are some snapshots of entertaining conversations with Banter AI.

The Future

Banter AI is like a ripple in a larger wave of AI innovation.

The next big step for Project Banter AI could be building a multi-agent system that combines more advanced AI algorithms with human-like abilities such as debating and co-creating content. The main goal is not just to provide accurate responses but to create an authentic and enjoyable exchange of information between algorithms and humans in a unique and engaging way.

💬 Try It Out Yourself

GitHub repo → https://github.com/hashwanthgogineni/BanterAI

Do You Want to Build Your Own Conversational AI? If you’re interested in building advanced chatbots. Please Drop us a line at sales@ipponusa.com

Additional Context & Reading

• Multiverse’s Tiny Animal Models: Their SuperFly (~94M params) and ChickBrain (~3.2B params) models are designed for local deployment—including chat, speech, and reasoning—without sacrificing performance. TechCrunch+2TechCrunch+2
• Google’s CTCL Synthetic Data Generator: A conditional, privacy-preserving way to synthesize behavioral datasets for AI training without real-user data. Google Research
• John Peel’s Fear the Year 2099 Series: A six-book set beginning with Doomsday (1999), continuing with Betrayal, Traitor, Revolution, Meltdown, and Firestorm—all themed around AI, identity, and digital threat.amazon.com+6Fantastic Fiction+6Rising Shadow+6