Understanding how context windows work — and how to use them well — is one of the most practical skills for getting good results from any LLM, including the models on the L&S AI Inference platform.
As a conversation grows, older turns remain in the context window. Near the limit, the model may:
The model doesn't warn you before this happens. Monitoring conversation length is your responsibility.
If you're used to tools like ChatGPT, Claude.ai, or Gemini, you may have developed habits that don't translate as well to smaller open-weight models. Some key differences:
Models like GPT-4o are trained to infer your intent even from incomplete instructions. Open-weight models are more literal — they do what you ask, not what you meant. Being explicit and specific is more important.
Instead of: "Help me with this code."
Try: "This Python function is returning None when I pass a dictionary. Find the bug and explain why it's happening."
Commercial frontier models are highly optimized for long multi-turn conversations. Open-weight models can degrade more noticeably as context grows. Shorter, more focused conversations produce more consistent results.
If a frontier model makes a mistake, it will often catch and correct itself. Open-weight models are more likely to commit to a wrong answer and build on it. If something looks wrong, explicitly correct it:
"That's not right — [explain the error]. Try again."
Don't assume the model will notice the mistake on its own.
A frontier model can usually follow a 10-part instruction reliably. Open-weight models do better with 2–3 focused instructions per message. Break complex requests into steps.
Before sending a complex message, ask yourself: