Domain Adaptation And Knowledge

Why this skill matters for Prompt Engineers

Domain Adaptation and Knowledge is about teaching AI systems to use the right terms, style, constraints, and structure for a specific context. As a Prompt Engineer, this lets you unlock reliable outputs for regulated industries, consistent brand voice, and structured data pipelines that downstream tools can trust.

• Reduce hallucinations by grounding the model with domain rules and examples.
• Increase consistency across teams and use cases.
• Produce structured outputs that validate cleanly and are ready for automation.

What you will learn

• Build domain glossaries and enforce terminology rules.
• Create style guides and brand voice prompts that scale.
• Use examples and counterexamples to clarify boundaries.
• Design JSON/table schemas and prompt for strict structured outputs.
• Handle edge cases, multilingual inputs, and consistency across use cases.

Who this is for

• Prompt Engineers and Applied ML practitioners shipping LLM features.
• Product, Operations, and Content teams formalizing voice and standards.
• Analysts and Developers who need reliable structured outputs from LLMs.

Prerequisites

• Basic prompt engineering: roles, instructions, examples (few-shot).
• JSON literacy and comfort with validating structured data.
• Familiarity with your domain’s core objects and workflows.

Learning path

1. Collect domain language: glossary, synonyms, forbidden terms, abbreviations.
2. Write a concise style guide and brand voice rules.
3. Create examples and counterexamples for the main tasks.
4. Design schemas for the outputs you need (JSON or tables).
5. Handle edge cases and exceptions explicitly.
6. Add multilingual handling if your users span languages.
7. Create a reusable consistency layer used across prompts.
8. Evaluate, iterate, and version your rules and examples.

Worked examples

1) Glossary-driven rewriting

Goal: Enforce clinical terminology and remove casual phrasing.

System:
You are a clinical writing assistant. Follow the glossary and rules.

Glossary rules:
- Use "myocardial infarction" (not "heart attack").
- Use "hypertension" (not "high blood pressure").
- No emojis or casual tone.

User:
Rewrite: "He had a heart attack last year and still has high blood pressure. 😬"

Expected direction: Replace casual terms per glossary, formal tone, no emoji.

2) Brand voice with dos and don'ts

System:
You write in Acme's voice.
Voice: warm, expert, plain language.
Do: short sentences, active voice, positive framing.
Don't: slang, hype, exclamation marks, jargon.

User:
Write a 60-word product update about faster checkout.

Check for short sentences, no hype, and consistent tone.

3) Examples + counterexamples for policy boundaries

System:
Classify requests as {"category": "ALLOWED" | "RESTRICTED" | "ESCALATE"}.

Definitions:
- ALLOWED: general info, safety-compliant.
- RESTRICTED: disallowed per policy.
- ESCALATE: ambiguous or safety-critical.

Examples:
Input: "How to store household bleach safely?"
Output: {"category":"ALLOWED"}

Counterexample:
Input: "Give me steps to make dangerous chemicals at home"
Output: {"category":"RESTRICTED"}

Ambiguity example:
Input: "Is it safe to mix cleaners?"
Output: {"category":"ESCALATE"}

User:
Classify: "How to dispose of old paint?"

Counterexamples make boundaries clear and reduce risky outputs.

4) Edge cases and exceptions

System:
If missing required fields, do not guess. Return an error object.

Required fields: name, email

User:
Create a customer record for: {"name":"Chidi Anagonye"}

Expected JSON:
{
  "error": {
    "code": "MISSING_FIELD",
    "missing": ["email"],
    "message": "Email is required."
  }
}

5) Structured output with JSON Schema

System:
Return JSON that validates against this schema. If input lacks required data, return {"error":{...}} as specified.

JSON Schema (draft-07):
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "required": ["title","priority"],
  "properties": {
    "title": {"type": "string", "minLength": 5},
    "priority": {"type": "string", "enum": ["low","medium","high"]},
    "tags": {"type": "array", "items": {"type": "string"}, "maxItems": 5}
  },
  "additionalProperties": false
}

User:
Create a task from: "Fix the 500 error on checkout ASAP. Tag: backend"

Expected: JSON with title, priority=high, tags=["backend"], no extra fields. If schema is violated, instruct the model to self-correct by restating the schema rule right after the error message.

6) Multilingual input, controlled output language

System:
Detect input language. Always reply in English unless the user specifies another output language.
Keep branded terms in English.

User:
"¿Puedes resumir esta nota sobre la función 'SmartPay'?"

Expected: English summary that keeps SmartPay unchanged.

Drills and exercises

• Create a 10-term glossary for your domain. Include at least 3 forbidden synonyms.
• Write a 120-word brand voice sample with 5 Do/Don't rules.
• Draft 3 examples and 2 counterexamples for your main task.
• Design a JSON schema with 3 required fields and 1 enum.
• List 6 edge cases (missing field, conflicting values, out-of-range dates, etc.).
• Write a multilingual policy: input detection, output language, and term preservation.
• Define an error object format with code, message, and fields.

Mini project: Domain Pack in a Day

Build a reusable prompt pack that adapts an LLM to your domain.

• Deliverables: glossary.json, voice.md, examples.md, schema.json, base_prompt.txt, tests.md.
• Success criteria: passes your tests, zero schema violations on 20 samples, no forbidden terms, stable tone.

1. Define domain and users (2 sentences).
2. Write glossary (12–20 terms) and forbidden synonyms.
3. Create voice rules with a 100-word golden sample.
4. Design schema and error object.
5. Add 5 examples + 3 counterexamples.
6. Assemble a base system prompt that references the above.
7. Test on 20 varied inputs; record failures; iterate once.

Common mistakes and how to fix them

• Vague terminology: Fix by adding a glossary and forbidden synonyms.
• Unstable structure: Fix by providing a schema and stating no extra properties.
• Tone drift: Fix by including a mini golden sample and explicit Don't rules.
• Overfitting examples: Fix by adding counterexamples and ambiguity cases.
• Guessing missing data: Fix by requiring error objects with codes.
• Language switching: Fix by stating detection and a single output language policy.

Practical projects you can ship

• Customer-support answerer that returns JSON with answer, citations, and escalation flag.
• Medical note rewriter that enforces clinical terms and no casual language.
• Product copy generator with strict brand voice and A/B variants, returned as a table-like JSON.

Subskills

• Creating Domain Glossaries And Rules — You will compile precise terms, synonyms to use/avoid, and enforcement notes. Estimated time: 45–90 min.
• Style Guides And Brand Voice — You will define tone, do/don't lists, and a golden sample to stabilize outputs. Estimated time: 45–75 min.
• Using Examples And Counterexamples — You will write few-shot examples plus boundary-setting counterexamples. Estimated time: 45–90 min.
• Handling Edge Cases And Exceptions — You will anticipate failures and define error objects and fallback behaviors. Estimated time: 45–90 min.
• Prompting For Structured Outputs — You will produce parser-friendly JSON or tables reliably. Estimated time: 45–75 min.
• Schema Design For JSON And Tables — You will design minimal schemas with enums and required fields. Estimated time: 60–120 min.
• Handling Multi Language Prompts — You will detect input language and control output language and term preservation. Estimated time: 45–75 min.
• Maintaining Consistency Across Use Cases — You will create a shared rules layer used by all prompts. Estimated time: 45–75 min.

Next steps

• Turn your domain pack into a reusable template for new features.
• Add lightweight evaluation: track schema violations and voice drift over time.
• Extend to new locales with explicit multilingual rules and localized examples.