**Overall: 41/50 — pipeline mostly healthy, one clean hallucination to fix.** The retrieval layer did its job well. Chunks 1 and 2 directly cover both sides of the comparison, and the model correctly ignored the billing chunk (Chunk 3) as irrelevant noise. The generated response is also well-structured and nearly complete. The single failure is unambiguous: **"Enterprise plans also include 24/7 phone support"** has no grounding in any retrieved chunk. The model reached into its parametric memory — likely pattern-matching against common enterprise SaaS feature sets — and injected a feature the documentation never mentions. This is exactly the failure mode RAG is supposed to prevent. **The fix is squarely in the generation layer:** 1. Tighten the system prompt with an explicit grounding constraint — something like *"only state facts present in the retrieved context; if unsupported, say so."* 2. Optionally layer in a post-generation NLI check that scores each response sentence against the context before it's served. The retrieval and context setup don't need changes. You could also tighten Chunk 3 out by raising your similarity threshold, but that's a minor quality-of-life improvement, not a correctness fix.