What Should Developers Consider When Building an AI Chatbot?

[jademerlin24]

I'm exploring modern AI chatbot development and would like to hear from developers who have experience building conversational applications.

Traditional chatbots usually depend on predefined rules and responses, while newer AI chatbots can use large language models to understand natural language and generate more flexible responses. However, building a reliable chatbot for real-world use seems to involve more than simply connecting an application to an AI model.

What approach would you recommend for handling the following?

Understanding user intent and context
Maintaining conversation history
Connecting the chatbot with APIs and databases
Using company-specific knowledge without generating incorrect information
Managing sensitive user data and API security
Handling situations where the AI cannot provide a reliable answer
Measuring response quality and chatbot performance

For developers who have built AI chatbots, would you recommend starting with a traditional intent-based architecture and gradually adding an LLM, or using an LLM-based architecture from the beginning?

I'd also be interested in hearing which programming languages, frameworks, or tools you've found most practical for production chatbot projects.

What lessons did you learn when moving from a basic chatbot prototype to a production-ready AI chatbot?

[rproffitt]

Right now this is bad idea because people can cause many of those chatbots to hallucinate and eat TOKENS.

Example at https://imgur.com/gallery/kit-boga-makes-ai-chatbot-go-crazy-hallucinate-kit-is-why-did-you-redeem-guy-who-scams-scammer-callers-aHTRl05

This is similar to the old SQL INJECTION problems and for now you have NO DEFENSE against this.

But hey, your TOKENS to get burned.

[jademerlin24]

One thing that seems increasingly important is treating the LLM as only one component of the overall chatbot architecture. A production-ready chatbot may also need a reliable knowledge source, conversation management, access controls, error handling, and clear fallback paths when the system cannot provide a trustworthy answer.

My initial thought is that an LLM-based approach could be useful from the beginning for handling natural conversations, while structured workflows or intent-based logic could still be used for specific actions that require predictable results. This hybrid approach may offer more flexibility without giving the model complete control over every interaction.

I’m also interested in how developers evaluate a chatbot before deploying it. Testing for response quality is one thing, but testing edge cases, incorrect assumptions, security boundaries, and failures in connected APIs seems equally important when moving from a prototype to a production environment.

Has anyone found a particular testing or evaluation approach especially useful for identifying these issues early?