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Prompt Master
Prompt Engineering 教程与提示词实战

从任务定义、示例和工作流到评测与安全边界

Prompt Engineering Tutorial and Prompt PracticeModels

Claude 4.5

Claude 4.5 Sonnet / Haiku capabilities and usage tips

CHAPTER PROMPT DECISION
01Prompt problem

Claude 4.5 Sonnet / Haiku capabilities and usage tips

02Reviewable output

A reusable prompt, example set, evaluation record or safety rule with its task boundary preserved.

03Definition of done

Run at least one representative case, inspect the result and record what still requires human review.

TL;DR

  • Claude 4.5 is Anthropic's 2025 flagship model. Current main SKUs: Sonnet 4.5 (primary) and Haiku 4.5 (lightweight/low-cost).
  • Good at: long document/table comprehension (200K default, optional 1M beta), structured output (JSON/tables), writing analysis, and stable code/tool calling.
  • Model selection advice: start with Haiku 4.5 to get things working, then upgrade to Sonnet 4.5 for quality. Use 10-50 samples for eval -- check accuracy, format stability, and hallucination rate.

Key Focus Areas

When reading this page, pay attention to:

  1. Context window (200K default, enterprise can request 1M beta)
  2. Structured output capabilities (JSON/Tool Calling stability)
  3. Hallucination and factual question answering performance
  4. Code/table comprehension: improvements over Claude 3.5

For production use, lock down the output format (schema) and add self-check (e.g., require evidence/quoted snippets).

What's New (4.5 vs 3.x/3.5)

  • Long context: 200K default; enterprise/specific endpoints can use 1M beta (requires dedicated Header).
  • More stable structured output: JSON/schema constraints drop fewer fields, function calling is more robust in loops/error recovery.
  • Code and tables: Better code comprehension/generation, more stable table/CSV extraction.
  • Safety and alignment: Fewer refusals, but still recommended to output "source/confidence" for auditing.

Usage Tips

  • Prompt structure: System message defines role/format/constraints, User provides context + task, add self-check when needed.
  • JSON/tool calling: Temperature 0-0.3; require the model to self-check field completeness before output; limit tool calling rounds to avoid loops.
  • Long documents: Summarize in sections then consolidate; for traceability, have the model output "quoted snippet + paragraph number."
  • Latency/cost: Haiku 4.5 for high concurrency; Sonnet 4.5 for critical quality steps.

References (Official)

📚 Related resources