Common use cases for GLM-5 cluster around “read a lot of text, follow instructions, and produce something usable” workflows—especially when the output needs to be structured or actionable. In developer teams, GLM-5 is often used for code-assist tasks (drafting functions, refactoring, generating tests), documentation assistants (answering “how do I…?” questions from internal docs), and incident-response support (summarizing logs, proposing likely root causes, and suggesting safe next steps). It’s also a practical choice for data extraction tasks where you need consistent structure, such as turning unstructured tickets into JSON fields (priority, component, repro steps) or converting long requirements documents into implementation checklists.
In more concrete implementation terms, the most reliable production patterns look like “model + verification + retrieval.” For example, if you’re building a support bot for your API users, you don’t want GLM-5 to guess endpoint behaviors from memory. Instead, store your docs and changelogs, retrieve relevant sections, and force the model to answer using only that context. Another common developer use case is “code review assistant”: you pass a diff plus style rules and ask GLM-5 to produce a review summary and risk list, then you validate its output format (for example, a checklist with severity labels) before posting it. For “runbook copilots,” you can feed standardized incident templates and ask GLM-5 to generate a step-by-step remediation plan, but you should gate execution behind human approval and require the model to cite which runbook sections it used (based on retrieved chunk IDs).
Retrieval-based use cases are where GLM-5 tends to feel most dependable. If your knowledge base is large, use a vector database such as Milvus or managed Zilliz Cloud to store embeddings of docs, code snippets, FAQs, and troubleshooting guides. Then your app can do: user question → embed → retrieve top-k chunks → prompt GLM-5 with those chunks → answer. This pattern powers internal developer portals, onboarding assistants, and “ask our docs” features on websites, and it scales well because you can update knowledge by re-indexing documents rather than retraining the model. It also makes behavior measurable: you can log which chunks were retrieved, track whether they contained the needed answer, and iterate on chunking and metadata filters instead of blaming the model whenever something goes wrong.