How to Integrate Web Search into a Conversational AI Chat: Product Design Best Practices

Introduction: Why Web Search in AI Chats Matters

Conversational AI tools are becoming essential for accessing information and services. However, they face a critical limitation: their knowledge base ends at the point of training. This means recent events, breaking news, or highly specific details remain inaccessible unless the system is directly integrated with web search.

From a product design perspective, this creates a user experience gap: what happens when the AI doesn’t know the answer? If the assistant stops being useful, frustration grows, and trust declines. That’s why integrating smart, transparent web search is no longer optional—it’s fundamental.

Main Approaches to Web Search in Conversational AI

Over the past years, three major approaches have emerged for blending search into conversational interfaces:

1. Conversational Search Integration

The AI turns search into natural dialogue, presenting results with explanations and cited sources (e.g., Perplexity).

2. Hybrid Assisted Search

Chat is combined with interactive elements such as snippets, charts, and dynamic filters to improve navigation.

3. Proactive AI Assistant

The system anticipates user needs and automatically triggers search without explicit input.

Best Practices for UX

Regardless of the approach, some design principles are crucial:

• Be transparent about sources.
• Handle errors gracefully (avoid conversational “dead ends”).
• Apply usage limits to prevent misuse.
• Personalize progressively to adapt results to user behavior.

My Experimental Solution: Transparent Integration

In my project, I opted for invisible integration:

• No visible buttons or toggles.
• If the AI detects a query about recent events or an explicit request for online information, it automatically triggers search.
• Otherwise, the flow remains natural and uninterrupted.

For sustainability, I also set a limit of 10 searches per month per user (free and premium). This helps me:

• Control costs (as a solo developer, I have no budget for pay-per-use APIs).
• Prevent the assistant from acting like a raw search engine, keeping it focused on conversation.

Why I Chose Tavily (vs Brave Search and Perplexity)

To power web search, I selected Tavily, an API designed specifically for AI agents and LLMs:

• Optimized for RAG (Retrieval-Augmented Generation).
• Provides summaries, not just links.
• Includes relevance scoring for better source filtering.
• Free plan: 1,000 credits/month, enough for testing.

Tavily vs Brave vs Perplexity

The three most interesting web search APIs today are Tavily, Brave Search, and Perplexity.

• Tavily is LLM-first, offering summaries and relevance scores, but has limited free credits.
• Brave Search is independent from Google/Bing, privacy-first, and offers up to 2,000 free queries monthly, though it requires more processing for AI pipelines.
• Perplexity combines search and generation, delivering contextualized answers, but it’s less flexible when used strictly as an API.

Conclusion: The Future of AI Search

Integrating web search into conversational AI is not just technical—it’s a product design challenge.

The key is to:

• Keep conversations smooth and natural.
• Maintain transparency about information sources.
• Apply simple but effective rules to limit abuse.
• Choose the right provider for your needs (Tavily for prototyping, Brave for high volume, Perplexity as a UX benchmark).

The future of AI search will be hybrid, blending model knowledge with real-time internet information—while keeping the user experience at the center.