Yes, GLM-5 can be used for code generation, but you’ll get the best results when you treat it like a code assistant that drafts changes and explains tradeoffs, not a “push to prod” autopilot. In practice, GLM-5 is useful for generating new modules from a spec, writing unit tests, refactoring functions, translating code between languages, and producing migration scripts. The biggest win comes from giving the model the right context and constraints: target language/runtime, project conventions, function signatures, and acceptance tests. If your prompt is vague (“write a backend”), you’ll get generic output; if your prompt is concrete (“add a POST /sessions endpoint; follow this existing router pattern; add tests; do not change public API”), you’ll get code that is far closer to mergeable.
A reliable implementation pattern is “generate → validate → iterate.” For example: (1) ask GLM-5 to propose a plan and a patch format (like a unified diff), (2) apply the patch in a sandbox branch, (3) run unit tests/lint/type checks, and (4) feed failures back to the model with the failing logs and constraints. This closed loop turns the model from a one-shot generator into an iterative helper that converges on correct code. If you are building an agent-style tool around GLM-5, function calling is a clean way to integrate steps like “run tests,” “search repository,” or “fetch dependency versions.”[Z.ai] (http://Z.ai) documents a function-calling capability that you can use to let the model request structured tool invocations rather than emitting brittle command strings.
Retrieval is also a major booster for code generation quality, especially when the model needs to follow your internal APIs and conventions. Instead of pasting large chunks of repository text into the prompt, embed your codebase and docs, store them in Milvus or managed Zilliz Cloud and retrieve the most relevant files/functions for the task (for example, “router patterns,” “auth middleware,” “error handling utilities,” “test helpers”). Then prompt GLM-5 with only those retrieved snippets plus a strict instruction like “use these utilities; do not invent new helpers.” This reduces hallucinated APIs and helps the model align with the existing design. It also makes your tool measurable: you can track whether retrieval returned the correct helper functions, and improve chunking/metadata filters when generation quality drops.