Model-Specific Prompt Adapter for Claude Template

This playbook is for engineering teams running multi-model architectures who need to adapt a base prompt for Anthropic's Claude models. The job-to-be-done is translating a prompt originally written for another model family (such as OpenAI or Gemini) into a structure that aligns with Claude's known behavioral preferences: XML-tagged sections for clear separation, system messages for behavioral contracts, and a preference for instructions that precede the content they govern. The ideal user is a developer or prompt engineer who already has a working prompt for another model and needs a reliable, repeatable adapter template rather than ad-hoc rewriting.

Use this template when you are migrating a prompt to Claude, maintaining a single prompt source-of-truth that must render correctly across model families, or building a prompt routing layer where the same logical task hits different models. The template expects you to provide the original prompt's core components—system instructions, user input, output schema, examples, and constraints—as structured variables. It then reassembles them in Claude-preferred order: system block first, followed by context and examples wrapped in XML tags, then the user query, and finally output formatting rules placed immediately before the expected response. Do not use this template when you are writing a prompt exclusively for Claude from scratch (use a native Claude template instead), when the prompt relies on model-specific features like OpenAI's structured outputs or Gemini's controlled generation that have no Claude equivalent, or when the task requires real-time tool-use orchestration that differs fundamentally between model families.

Before adopting the adapted prompt, you must run cross-model output equivalence tests. The adapter changes structure and ordering, not intent, but structural changes can shift model behavior in subtle ways. Set up a regression test suite that compares outputs from the original model and Claude on the same inputs, using an LLM judge or structured diff against your output schema. Pay special attention to instruction following, refusal rates, and format adherence. If your workflow is high-risk, require human review of adapted outputs until equivalence is confirmed across your eval set. The next section provides the copy-ready template you will wire into your prompt assembly pipeline.

This template is the core artifact of the playbook. It accepts a source prompt—originally written for another model family or as a generic instruction set—and rewraps it into a structure that aligns with Claude's documented behavior patterns. The template uses XML tags for semantic partitioning, places instructions in the system message where they are most stable, and reorders content so that Claude sees the task, constraints, and output format in the sequence that produces the most reliable results. Use this when you are porting an existing prompt to Claude or when you want a single canonical format for your Claude-bound requests.

textCopy
<system>
You are [ROLE_DESCRIPTION].

<behavior>
[TONE_AND_STYLE_INSTRUCTIONS]
</behavior>

<constraints>
[RULES_AND_BOUNDARIES]
</constraints>

<output_format>
[OUTPUT_SCHEMA]
</output_format>

<tools>
[TOOL_DEFINITIONS]
</tools>
</system>

<context>
[RETRIEVED_EVIDENCE_OR_BACKGROUND]
</context>

<examples>
[FEW_SHOT_EXAMPLES]
</examples>

<task>
[USER_INPUT_OR_TASK_DESCRIPTION]
</task>

To adapt this template, start by mapping your existing prompt components into the XML blocks. The <system> block is mandatory and should contain everything that defines the model's persistent behavior: role, tone, constraints, output format, and tool definitions. The <context> block holds retrieved evidence, documents, or background data. The <examples> block is optional but should be included whenever you have few-shot demonstrations; place them here rather than inline with the task. The <task> block carries the specific user input or instruction for this turn. If your source prompt uses markdown headers, JSON structures, or plain text delimiters, replace them with these XML tags. If your source prompt places instructions in the user message, move stable behavioral instructions into <system> and keep only the variable task in <task>. After assembly, validate that no square-bracket placeholders remain unresolved and that the total token count fits within your target model's context window.

This adapter is designed to sit between your canonical prompt template and the Claude model endpoint. The core job is to transform a model-agnostic prompt structure into Claude's preferred format: moving system-level instructions into the system parameter, wrapping complex constraints in <instructions> XML tags, and restructuring few-shot examples into a <examples> block with clear <example> delimiters. The adapter should be invoked as a preprocessing step before the inference call, not baked into the prompt template itself, so you can maintain a single source of truth for your prompt logic while supporting multiple model families.

In a production harness, wire the adapter as a function adapt_for_claude(canonical_prompt, runtime_context) that accepts your standard prompt object and returns a Claude-formatted payload. Validate the output before sending: check that all square-bracket placeholders are resolved, the system field is populated if your canonical template includes system instructions, and the total token count falls within Claude's context window. Implement a retry wrapper that catches 400 errors from malformed XML or schema mismatches, logs the adapted prompt for debugging, and falls back to a simpler format if the structured XML approach fails. For high-stakes workflows, add a preflight validation step that sends a small subset of test cases through the adapter and checks for output schema conformance before deploying the adapted prompt to production traffic.

The most critical production concern is cross-model output equivalence. After adapting a prompt for Claude, you must verify that Claude's outputs match the expected schema and behavioral contract defined by your canonical template. Build an eval harness that runs the same test inputs through both your primary model and the Claude-adapted path, then compares outputs using your standard LLM judge or schema validator. Flag any divergence in refusal rates, output structure, or instruction following. If Claude consistently underperforms on certain prompt patterns, consider maintaining a Claude-specific variant of the canonical template rather than relying solely on the adapter. Avoid the trap of silently accepting degraded Claude outputs because the adapter 'worked'—the adapter's job is to preserve behavioral equivalence, not just syntactic compatibility.