What are common failure patterns when using GLM-5?

Common GLM-5 failure patterns fall into a few predictable buckets: context mismatch, tool misuse, format drift, latency blow-ups, and unverified answers. None of these are unique to GLM-5; they’re the standard failure modes of deploying any powerful text model in production. The good news is they are engineering problems with engineering fixes. GLM-5’s large context and agentic positioning can reduce some errors (it can follow longer instructions and handle multi-step tasks better), but if you don’t add guardrails, it can still produce confident wrong answers or outputs that don’t match your required schema.

Here’s a concrete list of failure patterns and how they show up in real systems:

1. “Looks right but not in our docs” (hallucination / version drift)
   The model answers using generic knowledge instead of your product’s current docs. Fix: RAG + metadata filters + “use only Context” rules.

2. Invalid structured output (JSON/Markdown schema drift)
   The model returns almost-valid JSON, extra commentary, or missing keys. Fix: strict schema + validator + automatic re-prompt on parse errors.

3. Tool-call argument errors
   Tool calls have wrong parameter names/types, or the model calls tools unnecessarily. Fix: strict JSON schema validation + tool allowlists + “call tools only when needed” prompt rules. See Z.ai’s tool calling docs: Function Calling.

4. Latency spikes under load
   Requests with long context/output cause KV cache pressure and batching instability. Fix: cap tokens, stream, tune inference server concurrency (e.g., vLLM max_num_seqs, max_num_batched_tokens, gpu_memory_utilization): vLLM Optimization and Tuning.

5. Multi-turn drift
   In longer conversations, the model contradicts earlier constraints or forgets a decision. Fix: carry a compact “state object” each turn and avoid appending endless transcripts.

A useful internal “incident form” for model failures is:

• Failure type: ___

• Retrieved chunk IDs (if RAG): ___

• Prompt template version: ___

• Model revision/config: ___

• Validator result: ___

• Fix applied (chunking/filter/prompt/validator): ___

This makes failures actionable instead of vague.

On Milvus.io, the most common root cause is usually retrieval quality, not model intelligence. If your docs are indexed in Milvus or Zilliz Cloud, many “bad answers” trace back to: wrong chunk size, missing metadata filters (version/lang), or low-quality embeddings for code-heavy docs. The fix is to improve retrieval: better chunking by headings, store version fields, and evaluate retrieval hit rate on real queries. Once retrieval reliably returns the right chunks, GLM-5’s job becomes much easier: synthesize from sources, follow formatting rules, and stop guessing. That’s the most consistent path to stable production behavior.