GLM-5 has the same core limitations as any LLM used in production: it generates outputs based on learned patterns, not guaranteed truths, so it can be confidently wrong, especially when your prompt lacks grounding context. Even in coding tasks, it may produce code that compiles but is logically incorrect, insecure, or inconsistent with your project’s conventions. For workflows that require correctness—database migrations, auth logic, billing—you should treat GLM-5 as an assistant that drafts and explains, not as an authority. The right way to use it is to add verification steps: unit tests, type checks, linters, schema validation, and human review for high-impact changes. Z.ai’s own positioning emphasizes long-horizon agent tasks, but “long-horizon” also means there are more opportunities for drift unless you build guardrails.
A second limitation is operational: large MoE checkpoints and long-context inference are engineering-heavy. Even with MoE (where only some parameters are active per token), you still have to store and load a large model, distribute it across hardware, and manage performance under concurrency. Long context increases memory pressure (KV cache) and can increase latency, especially when many users hit the system at once. Attention optimizations like DSA are meant to help, but you should still expect careful tuning: max context lengths, truncation rules, batching, streaming, and per-tenant quotas. If you self-host, you also need to handle model upgrades, reproducibility (pinning exact revisions), and incident response when a model behavior changes under new weights or runtime versions.
A third limitation is knowledge freshness and specificity. GLM-5 will not automatically know your private documentation, internal APIs, or the latest changes in your product unless you provide that information. This is where retrieval becomes not optional but foundational. If you skip retrieval, you’ll either (1) pass huge prompts and still miss key facts, or (2) get plausible-sounding but incorrect answers. The standard fix is to build RAG with a vector database such as Milvus or managed Zilliz Cloud: embed your documents, store them with metadata, retrieve the relevant chunks at query time, and prompt GLM-5 to answer only from those chunks. That doesn’t eliminate errors, but it dramatically improves traceability—when something is wrong, you can inspect the retrieved context and adjust chunking, filtering, or prompts rather than guessing why the model hallucinated.