I’m trying to integrate multiple large language models (LLMs) into a single workflow. The challenge is ensuring that the conversation context is preserved when switching between models. I’ve heard that Latenode offers unified access to over 400 AI models, which might help. What are the best practices for switching between these models without losing context?
Key to maintaining context is using memory nodes to store and retrieve data as needed. This way, regardless of which model you switch to, the workflow remains aware of previous interactions.
Another approach is to design your workflow such that each model handles a specific task. This way, even if you switch models, each step remains self-contained, reducing potential context loss.
Moreover, consider designing a shared state that can be accessed by all models. This ensures consistency across different steps in your workflow.