When you start working with LLMs like GPT-4, Claude, or Mistral, it's tempting to treat them like magic boxes: prompt in, gold out. But in real-world development workflows, one model isn’t enough. You quickly realize that every model has strengths and weaknesses – and that the real challenge is orchestration. That’s where platforms like ComposableAI come in: They let you combine different LLMs, assign them to tasks, and control your flow with real logic and quality checks.
Take a typical coding use case: one LLM handles boilerplate generation, another does code explanation for junior devs, and a third checks logic or security issues. You might want Mistral for speed and cheap token cost, but GPT-4 for anything customer-facing. With ComposableAI’s approach, you define the flow once and switch models like plug-and-play. Check out some of the architectures we’re building and exploring on our AI news & updates page.
Most importantly, this lets you move from hacky proof-of-concepts to scalable pipelines. Your AI assistants become part of your CI/CD process, of your documentation strategy, even your test suite. Thinking in prompts is fun – but thinking in systems is what gets you to production. For deeper dives, implementation tips, and example flows, check out the Composable AI developer blog and stay up to date via our latest releases and integrations.
