22. Building a Prompt Kit

Chapter 22 of 25 · 15 min

KEY INSIGHT

The value of a prompt kit compounds when prompts share input/output schemas, enabling composition and reuse across tasks. ```python # prompt_kit/ # ├── __init__.py # ├── schemas.py # ├── templates/ # │ ├── __init__.py # │ ├── classifier.py # │ ├── summarizer.py # │ └── extractor.py # ├── test/ # │ ├── test_classifier.py # │ └── test_summarizer.py # └── deploy.py # schemas.py - shared input/output definitions from pydantic import BaseModel, Field from typing import Literal class TextInput(BaseModel): """Standard input for text processing prompts.""" text: str = Field(min_length=10, max_length=10000) language: Literal['en', 'es', 'fr', 'de'] = 'en' class ClassificationOutput(BaseModel): """Standard output for classification tasks.""" label: str confidence: float = Field(ge=0.0, le=1.0) reasoning: str class ExtractionOutput(BaseModel): """Standard output for extraction tasks.""" entities: list[dict] = Field(default_factory=list) relationships: list[dict] = Field(default_factory=list) evidence: list[str] = Field(default_factory=list) # templates/classifier.py from .schemas import TextInput, ClassificationOutput classifier_prompt = """Analyze the following text and classify it according to the scheme below. Classification scheme: - positive: expresses satisfaction, includes purchase intent - negative: expresses dissatisfaction, includes complaint - neutral: informational only, no sentiment signal Text [{language}]: {text} Output your classification with confidence and reasoning.""" def classify(model, text_input: TextInput) -> ClassificationOutput: """Classify text using model with typed interface.""" rendered = classifier_prompt.format( language=text_input.language, text=text_input.text ) raw_output = model.generate(rendered) # Parser extracts fields into ClassificationOutput return parse_classification(raw_output) ``` **Failure mode:** Prompt kits without schema enforcement produce inconsistent outputs that break downstream consumers. A template that sometimes outputs JSON and sometimes outputs natural language will cause type errors in consuming code. ```python # Schema enforcement catches inconsistent outputs from pydantic import ValidationError def enforce_schema(prompt_template, output_parser, model): """Wrap template to guarantee schema-compliant output.""" def wrapped(**kwargs): raw_output = model.generate(prompt_template.render(**kwargs)) try: return output_parser.parse(raw_output) except ValidationError as e: raise PromptOutputSchemaError( f"Output does not match schema: {e}", raw_output=raw_output, prompt_name=prompt_template.name ) return wrapped ``` A prompt kit with 4 tasks (classifier, summarizer, extractor, generator) shared across 3 projects reduced per-project integration time from 3 days to 4 hours. The 87% time reduction came from schema reuse and shared testing infrastructure.

A prompt kit is a curated collection of production-ready prompts with shared infrastructure: testing harness, versioning, documentation, and deployment integration.

EXERCISE

Initialize a prompt kit for your most common use case. Create three templates with shared input schema, write a test harness that runs all templates against a common test set, and document the structure for team use.