llm-patterns

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# LLM Patterns Skill


For AI-first applications where LLMs handle logical operations.

---

## 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

---

## 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
```

---

## LLM Client Pattern

### Typed LLM Wrapper
```typescript
// 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-4-6',
  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);
}
```

### Structured Outputs
```typescript
// core/llm/schemas.ts
import { z } from 'zod';

export const ClassificationSchema = z.object({
  category: z.enum(['support', 'sales', 'feedback', 'other']),
  confidence: z.number().min(0).max(1),
  reasoning: z.string(),
});

export type Classification = z.infer<typeof ClassificationSchema>;
```

---

## Prompt Patterns

### Template Functions
```typescript
// core/prompts/classify.ts
export function classifyTicketPrompt(ticket: string): string {
  return `Classify this support ticket into one of these categories:
- support: Technical issues or help requests
- sales: Pricing, plans, or purchase inquiries

From alinaqi/maggy, MIT.

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