I’ve been experimenting with DEVONthink 4 AI capabilities and trying to determine when to use the ChatGPT Chatbox interface vs using DT4’s API integration to ChatGPT.
I found this article and video on the topic very helpful.
- https://www.youtube.com/watch?v=dOfiBS_SE3E
- The ChatGPT to API Transition: When & How to Make the Jump
I’d like to get DevonTechnologies perspective as well as DT4 users who are working with the AI.
Question: What are the use cases and benefits of using DT4’s AI LLM integration vs using the chatbot provided by the LLM?
Here’s a summary of key points from the video for quick scanning.
Takeaways
- Chatbot ≠ Full Product: Chatbots like ChatGPT and Claude are intentionally limited demos. The API exposes deeper capabilities, from custom system prompts to richer context control.
- More Control, More Power: APIs unlock tools like function calling, structured outputs, and streaming responses—giving you flexibility beyond the fixed presets of chatbot interfaces.
- Cost Transparency: Unlike flat monthly subscriptions, API usage charges are pay-as-you-go. For many workflows, especially with smaller models, API costs can be cheaper than subscriptions.
- Extended Context Windows: Large-scale tasks like feeding long documents or running reasoning-heavy workflows are only practical with API access, not the chatbot.
- Workflow Thinking vs. Chat Thinking: Chatbots encourage Q&A interactions; APIs shift users toward workflow design—input, process, output, integration—which scales better for real work.
- When Not to Switch: If you only use AI for casual brainstorming or conversational use, APIs won’t add much value. Stay with the chatbot until you hit friction.
- Accessible Entry Point: Modern LLMs can teach you how to use APIs step by step. You don’t need to be a developer—fear of code is outdated when the model can guide you.
Summary
In this talk, I explain why using an API is different from, and often more powerful than, staying inside a chatbot. Chatbots like ChatGPT and Claude are limited demos; APIs unlock features like function calling, custom system prompts, extended context windows, and cost transparency. APIs help shift your mindset from chat interactions to workflow design, enabling richer integrations and more control. But the transition isn’t for everyone—if you only use AI casually, the chatbot is enough. The right time to switch is when you hit friction the chat interface can’t solve, and today’s LLMs can guide you step by step.