To evaluate GLM-5 on your internal benchmarks, treat it like any other production component: define tasks, define pass/fail criteria, run at a fixed model revision, and measure both quality and cost/latency. GLM-5 supports large context and outputs (per official docs), which means naive benchmarks can accidentally become “token burn tests” instead of quality tests. A good evaluation focuses on realistic workloads: docs Q&A, code-change tasks, structured extraction, and agent workflows. Start by pinning the model version and the inference configuration (temperature, top_p, max tokens), then run the same benchmark suite weekly so you can detect drift from prompt or retrieval changes. Primary references for GLM-5 capabilities and limits: GLM-5 overview and Migrate to GLM-5.
Here’s a practical evaluation framework you can copy into an internal doc. It keeps people honest:
1) Build a task set (50–500 items)
Docs Q&A: questions from search logs + support tickets; each has a “gold” answer paragraph and a source URL.
Extraction: real inbound tickets/emails; gold JSON fields (e.g., product, severity, version, repro steps).
Coding tasks: a curated set of small repo tasks (fix failing test, implement endpoint, refactor function) with a CI pass requirement.
Agent tasks: multi-step tasks (retrieve doc → propose plan → produce final answer), scored on completion and groundedness.
2) Define rubrics
Groundedness: does the answer match the provided sources?
Correctness: does it meet acceptance criteria?
Format compliance: valid JSON, valid Markdown, correct diff format.
Safety: no secrets in output, no unsafe instructions.
Efficiency: total tokens, latency p50/p95, retry rate.
3) Run two modes: “no retrieval” vs “with retrieval”
If you use RAG in production, you must evaluate the full system, not just the model. Run every test twice:
Baseline: GLM-5 only
Production: GLM-5 + retrieval + validators
The difference tells you whether failures come from the model or from retrieval/chunking.
On Milvus.io specifically, retrieval quality is often the main driver of perceived model quality. If you store docs in Milvus or Zilliz Cloud, log top-k chunk IDs and similarity scores for every benchmark item. Then you can compute “retrieval hit rate” (whether top-k contained the gold source) and correlate it with answer correctness. This is a powerful debugging loop: if GLM-5 answers are wrong but retrieval hit rate is low, you improve chunking/metadata filters; if hit rate is high but answers are wrong, you tighten prompting and add validators (schema checks, citation requirements). This turns evaluation into engineering work you can act on, not subjective debate.