GLM-5 generates responses by taking your prompt (or chat messages) as tokens, running them through a large transformer model, and then producing the next tokens autoregressively until it reaches a stop condition (stop tokens, max tokens, etc.). In practice, you provide a system instruction (rules), user input (task), and optionally tool definitions (functions). GLM-5 then predicts the most likely next token sequence given that input, with sampling settings like temperature and top-p controlling how deterministic or diverse the output is. For many developer workflows, you’ll keep temperature lower and enforce formatting constraints, because “slightly creative” is usually worse than “consistently correct.”
Two implementation details matter a lot for developers: context length and tool calling. GLM-5 is documented with a very large context window (the developer docs list 200K context and large output token limits), which means it can read long inputs like multi-section specs or large retrieved context packs. But long context also increases latency and memory cost, so you should avoid dumping everything into the prompt if only a few parts are relevant. Tool calling is the other key: rather than forcing the model to guess missing facts, you can define tools and let the model call them. Z.ai’s docs describe a function-calling interface with tools and tool_choice, and a migration guide that notes GLM-5 can stream tool-call parameter construction when stream=True and tool_stream=True. In production, that’s useful because you can see what arguments the model is building and apply validation before executing tools.
For real applications (especially on websites), the strongest pattern is “retrieve, then generate.” If a user asks a question about your docs or product behavior, don’t rely on the model’s pretrained knowledge alone. Instead, retrieve relevant sections from a vector database such as Milvus or Zilliz Cloud, then ask GLM-5 to answer strictly from that context. This approach makes the model’s generation step more like “compose an answer from sources” and less like “improvise.” A concrete prompt structure that works well is:
System: output rules, tone, “use only provided context,” formatting constraints
Context: retrieved chunks with titles/URLs/version metadata
User: the question and any required output format (e.g., JSON schema)
For more details on GLM-5 usage and capabilities, see GLM-5 developer overview, the function calling guide, and the official quick start.