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Building Conversational AI Apps

Create engaging conversational experiences using Synqly

By Synqly TeamUpdated December 2025

Conversational apps are more than “send prompt → show response”. Great experiences have: • Fast feedback (streaming, typing indicators) • Clear system behavior (tone, safety, constraints) • Strong context handling (history, summaries) • Reliability (retries, fallbacks) This guide focuses on practical patterns you can ship: how to store messages, stream tokens, craft system prompts, and keep costs under control.

Conversation Architecture

A well-designed conversational AI application consists of several key components: • Message history management • Context window optimization • Streaming response handling • State persistence • Error recovery

Pro Tip

Keep conversation history under 4000 tokens for optimal performance and cost management.

Managing Message History

Maintain conversation context by storing message history:

TypeScript
interface Message {
  role: 'user' | 'assistant' | 'system';
  content: string;
  timestamp?: Date;
}

class ConversationManager {
  private messages: Message[] = [];
  private maxMessages = 20;

  addMessage(role: Message['role'], content: string) {
    this.messages.push({ role, content, timestamp: new Date() });
    
    // Keep only recent messages to manage context window
    if (this.messages.length > this.maxMessages) {
      this.messages = this.messages.slice(-this.maxMessages);
    }
  }

  getMessages(): Message[] {
    return this.messages;
  }

  clear() {
    this.messages = [];
  }
}

Implementing Streaming Responses

Stream responses for real-time user feedback:

TypeScript
async function streamChat(messages: Message[]) {
  const stream = await synqly.chat.completions.create({
    model: 'gpt-4',
    messages,
    stream: true
  });

  let fullResponse = '';

  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content || '';
    fullResponse += content;
    
    // Update UI with each chunk
    updateChatUI(content);
  }

  return fullResponse;
}

Crafting Effective System Prompts

System prompts define your AI assistant's personality and behavior:

TypeScript
const systemPrompt: Message = {
  role: 'system',
  content: `You are a helpful customer support assistant for Synqly.
  
Guidelines:
- Be concise and friendly
- Provide code examples when relevant
- Ask clarifying questions if needed
- Admit when you don't know something
- Direct users to documentation for complex topics

Tone: Professional yet approachable`
};

const messages = [
  systemPrompt,
  ...conversationHistory
];

UI Best Practices

Create an intuitive chat interface: • Show typing indicators during streaming • Display timestamps for context • Allow message editing and regeneration • Implement copy-to-clipboard for responses • Add loading states and error messages • Support markdown rendering • Enable code syntax highlighting

Safety & Quality Guardrails

For production chat apps: • Add moderation for user input if needed • Validate tool/function outputs • Strip secrets (API keys, tokens) from prompt context • Define a fallback response for outages • Add a human escalation path for sensitive workflows

State Persistence

Store conversations for user continuity:

TypeScript
// Save conversation to database
async function saveConversation(userId: string, messages: Message[]) {
  await db.conversations.create({
    userId,
    messages: JSON.stringify(messages),
    lastActive: new Date()
  });
}

// Load conversation history
async function loadConversation(userId: string, conversationId: string) {
  const conversation = await db.conversations.findOne({
    userId,
    id: conversationId
  });
  
  return JSON.parse(conversation.messages);
}