Building an AI Chatbot Product: Lessons from the Trenches

The Challenge

When I joined EasyAI, the chatbot product was handling basic FAQ responses. The vision was to transform it into an intelligent assistant capable of complex customer inquiries, data analysis, and multi-turn conversations.

Architecture Decisions

Streaming vs Polling

We moved from polling-based responses to Server-Sent Events (SSE) for real-time message streaming. The difference in user experience was dramatic — users could see the AI "thinking" in real-time instead of staring at a loading spinner.

Context Window Management

LLMs have token limits. We implemented a sliding window approach that keeps the most relevant conversation history while staying within context limits:

function buildContext(messages: Message[], maxTokens: number): Message[] {
  const systemPrompt = messages[0]; // Always keep system prompt
  const recent = [];
  let tokenCount = estimateTokens(systemPrompt.content);

  for (let i = messages.length - 1; i > 0; i--) {
    const msgTokens = estimateTokens(messages[i].content);
    if (tokenCount + msgTokens > maxTokens) break;
    recent.unshift(messages[i]);
    tokenCount += msgTokens;
  }

  return [systemPrompt, ...recent];
}

Error Handling at Scale

AI APIs fail. Rate limits hit. Models hallucinate. We built retry logic with exponential backoff, fallback models, and content safety filters. The key lesson: never trust AI output without validation.

Key Takeaways

1. Start simple — A well-prompted GPT-3.5 often beats a poorly-prompted GPT-4
2. Measure everything — Response time, accuracy, user satisfaction
3. Design for failure — AI is probabilistic, your error handling shouldn't be