> ## Documentation Index
> Fetch the complete documentation index at: https://launchdarkly-preview.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Building a chatbot with multiple AI providers using AI Configs: Before you ship

> This guide teaches you how to build a simple AI-powered chatbot using LaunchDarkly AI Configs with multiple AI providers, including Anthropic, OpenAI, and Google. This page covers before you ship.

> For the complete documentation index, see [llms.txt](/llms.txt). For the full documentation snapshot, see [llms-full.txt](/llms-full.txt).

## Before you ship

You built a working chatbot. When building your own application, check the following before releasing it to end users.

### Your config is actually being used

<CodeGroup>
  ```python title="Python" theme={null}
      config, tracker = ai_client.completion_config(key, context, fallback)

      # Log this somewhere visible
      print(f"Using model: {config.model.name}")
      print(f"Provider: {config.provider.name}")
  ```
</CodeGroup>

If you always see the fallback model, your LaunchDarkly connection is not working.

### Errors do not crash the application

<CodeGroup>
  ```python title="Python" theme={null}
      try:
          response = provider.generate(config, user_message)
      except Exception as e:
          logger.error(f"AI generation failed: {e}")
          response = "Sorry, I'm having trouble right now. Try again?"
  ```
</CodeGroup>

AI providers can become unavailable. Your application should continue to function.

### You have a rollout plan

Do not roll out changes to all users at once:

1. Test with your internal team at 5%
2. Roll out to beta users at 25%
3. Monitor error rates and token usage
4. Gradually increase to 100%

LaunchDarkly makes this easy with percentage rollouts on the **Targeting** tab.

### Online evals are considered

You will not know if your AI is giving good answers unless you measure it. Consider adding online evaluations once you are live. Read [when to add online evals](/tutorials/when-to-add-online-evals) for guidance.

## When AI Configs make sense

AI Configs are not the right fit for every project. Here are some common use cases:

### You're experimenting with prompts

If you are updating prompts frequently, hardcoding them can slow iteration. AI Configs let you test different prompts without redeploying.

**Example**: You run a customer support chatbot. You want to test whether a formal tone or casual tone works better. With AI Configs, you create two variations and switch between them from the dashboard.

### You need different AI behavior for different users

Free users get faster, lower-cost responses. Paid users get slower, higher-quality responses.

**Example**: A SaaS application with tiered pricing. The free tier uses `gpt-4o-mini` with temperature 0.

3. The premium tier uses `claude-3-5-sonnet` with temperature 0.

4. You target based on the `tier` attribute in the user context.

### You want to switch providers without code changes

Your primary provider is unavailable. You need to switch to a backup immediately.

**Example**: Anthropic has an outage. You log in to LaunchDarkly, change the default variation from Anthropic to OpenAI, and save. All requests now use OpenAI, with no redeployment needed.

### You're running cost optimization experiments

You think a lower-cost model might perform well enough for most queries. You want to test it with real traffic.

**Example**: You create one variation using `claude-3-haiku` and another using `claude-3-5-sonnet`. You roll out the lower-cost model to 20% of users and compare quality metrics.

### When AI Configs might be overkill

* **One-off batch jobs**: If you're processing 10,000 documents once, hardcoding the configuration may be sufficient.
* **Single model, no experimentation**: If you're using one model and do not plan to change it, AI Configs may add unnecessary complexity.

### Completion mode versus agent mode

If you're using LangGraph or CrewAI, use [agent mode](/guides/ai-configs/agent-vs-completion) instead of the completion mode shown in this tutorial.

This tutorial uses **completion mode** with messages in array format. If you're building:

* **Simple chatbots, content generation, or single-turn responses**: Use completion mode
* **Complex multi-step workflows with LangGraph, CrewAI, or custom agents**: Use [agent mode](/guides/ai-configs/agent-vs-completion)

The choice depends on your architecture. If you're calling `client.chat.completions.create()` or a similar method, completion mode is probably the right choice.

## Conclusion

In this guide, you learned how to:

* Build an AI chatbot with support for multiple providers
* Create and manage AI variations in LaunchDarkly
* Use contexts for targeted AI behavior
* Monitor AI performance and usage

LaunchDarkly AI Configs let you manage AI behavior across multiple providers without code changes, so you can iterate more quickly and deploy more safely.