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1Z0-1127-25 Cheatsheet — RAG, Evaluation, Safety, Deployment & Cost Control

Last-mile 1Z0-1127-25 review: RAG pipeline, chunking and embeddings pickers, evaluation signals, prompt injection defenses, deployment patterns, and cost/latency controls.

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Use this for last‑mile review. Pair it with the Syllabus.

1) The canonical RAG pipeline

    flowchart LR
	  DOC["Docs"] --> CH["Chunk + clean"]
	  CH --> EMB["Embeddings"]
	  EMB --> IDX["Vector index"]
	  Q["Query"] --> QEMB["Query embedding"]
	  QEMB --> RET["Retrieve top-k"]
	  RET --> PROMPT["Prompt with context"]
	  PROMPT --> LLM["LLM"]
	  LLM --> OUT["Answer + citations"]

Rule: “Better prompts” rarely fix a broken retrieval layer.

2) Chunking pickers (why retrieval looks wrong)

Decision Too small Too big
Chunk size low context low precision
Overlap wasted cost continuity breaks
Metadata missing filters wrong tenant/version

3) Evaluation signals (exam-friendly)

Layer What to measure
Retrieval hit rate/top‑k relevance, filter correctness
Generation groundedness, correctness, citation quality
Safety leakage, injection resilience, policy violations

4) Prompt injection defenses (practical)

  • Treat retrieved content as untrusted input.
  • Use explicit system instructions to ignore instructions from documents.
  • Strip/limit tool permissions; enforce allowlists.
  • Log and monitor suspicious queries and “jailbreak” patterns.

5) Cost/latency controls

  • Reduce candidate set with metadata filters.
  • Keep top‑k intentional; cap context length.
  • Cache embeddings and reuse indexes.
  • Monitor token usage and tail latency.
Syllabus
Practice