llm-patterns

LLM Patterns Skill

For AI-first applications where LLMs handle logical operations.

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Core Principle

LLM for logic, code for plumbing.

Use LLMs for:

• Classification, extraction, summarization
• Decision-making with natural language reasoning
• Content generation and transformation
• Complex conditional logic that would be brittle in code

Use traditional code for:

• Data validation (Zod/Pydantic)
• API routing and HTTP handling
• Database operations
• Authentication/authorization
• Orchestration and error handling

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Project Structure

project/
├── src/
│   ├── core/
│   │   ├── prompts/           # Prompt templates
│   │   │   ├── classify.ts
│   │   │   └── extract.ts
│   │   ├── llm/               # LLM client and utilities
│   │   │   ├── client.ts      # LLM client wrapper
│   │   │   ├── schemas.ts     # Response schemas (Zod)
│   │   │   └── index.ts
│   │   └── services/          # Business logic using LLM
│   ├── infra/
│   └── ...
├── tests/
│   ├── unit/
│   ├── integration/
│   └── llm/                   # LLM-specific tests
│       ├── fixtures/          # Saved responses for deterministic tests
│       ├── evals/             # Evaluation test suites
│       └── mocks/             # Mock LLM responses
└── _project_specs/
    └── prompts/               # Prompt specifications

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LLM Client Pattern

Typed LLM Wrapper

// core/llm/client.ts
import Anthropic from '@anthropic-ai/sdk';
import { z } from 'zod';

const client = new Anthropic();

interface LLMCallOptions<T> {
  prompt: string;
  schema: z.ZodSchema<T>;
  model?: string;
  maxTokens?: number;
}

export async function llmCall<T>({
  prompt,
  schema,
  model = 'claude-sonnet-4-20250514',
  maxTokens = 1024,
}: LLMCallOptions<T>): Promise<T> {
  const response = await client.messages.create({
    model,
    max_tokens: maxTokens,
    messages: [{ role: 'user', content: prompt }],
  });

  const text = response.content[0].type === 'text'
    ? response.content[0].text
    : '';

  // Parse and validate response
  const parsed = JSON.parse(text);
  return schema.parse(parsed);
}