async function generateResponse( systemPrompt: string, userMessage: string, options?: { maxTokens?: number; temperature?: number } ): Promise { const response = await client.messages.create({ model: "claude-sonnet-4-20250514", max_tokens: options?.maxTokens ?? 1024, temperature: options?.temperature ?? 0, system: systemPrompt, messages: [{ role: "user", content: userMessage }], });

const textBlock = response.content.find(block => block.type === "text"); return textBlock?.text ?? ""; }


## Streaming Responses

```typescript
async function streamResponse(
  messages: Array<{ role: "user" | "assistant"; content: string }>,
  onChunk: (text: string) => void
): Promise<string> {
  const stream = client.messages.stream({
    model: "claude-sonnet-4-20250514",
    max_tokens: 4096,
    messages,
  });

  let fullText = "";

  for await (const event of stream) {
    if (event.type === "content_block_delta" && event.delta.type === "text_delta") {
      onChunk(event.delta.text);
      fullText += event.delta.text;
    }
  }

  return fullText;
}

const response = await streamResponse(
  [{ role: "user", content: "Explain async/await in TypeScript" }],
  (chunk) => process.stdout.write(chunk)
);

Function Calling (Tool Use)

const tools: Anthropic.Tool[] = [
  {
    name: "search_database",
    description: "Search the product database by name, category, or price range",
    input_schema: {
      type: "object" as const,
      properties: {
        query: { type: "string", description: "Search query" },
        category: { type: "string", description: "Product category filter" },
        max_price: { type: "number", description: "Maximum price" },
      },
      required: ["query"],
    },
  },
];

async function agentLoop(userMessage: string): Promise<string> {
  const messages: Anthropic.MessageParam[] = [
    { role: "user", content: userMessage },
  ];

  while (true) {
    const response = await client.messages.create({
      model: "claude-sonnet-4-20250514",
      max_tokens: 4096,
      tools,
      messages,
    });

    if (response.stop_reason === "end_turn") {
      const text = response.content.find(b => b.type === "text");
      return text?.text ?? "";
    }

    const toolUse = response.content.find(b => b.type === "tool_use");
    if (!toolUse || toolUse.type !== "tool_use") break;

    const result = await executeToolCall(toolUse.name, toolUse.input);

    messages.push({ role: "assistant", content: response.content });
    messages.push({
      role: "user",
      content: [{ type: "tool_result", tool_use_id: toolUse.id, content: result }],
    });
  }

  return "";
}

RAG Pipeline

import { embed } from "./embeddings";

interface Chunk {
  id: string;
  text: string;
  metadata: Record<string, string>;
  embedding: number[];
}

async function retrieveAndGenerate(query: string): Promise<string> {
  const queryEmbedding = await embed(query);

  const relevantChunks = await vectorDb.search({
    vector: queryEmbedding,
    topK: 5,
    filter: { source: "documentation" },
  });

  const context = relevantChunks
    .map((chunk, i) => `[${i + 1}] ${chunk.text}`)
    .join("\n\n");

  const response = await client.messages.create({
    model: "claude-sonnet-4-20250514",
    max_tokens: 2048,
    system: `Answer questions using the provided context. Cite sources with [n] notation. If the context doesn't contain the answer, say so.`,
    messages: [
      {
        role: "user",
        content: `Context:\n${context}\n\nQuestion: ${query}`,
      },
    ],
  });

  return response.content[0].type === "text" ? response.content[0].text : "";
}

Document Chunking

function chunkDocument(
  text: string,
  options: { chunkSize: number; overlap: number }
): string[] {
  const { chunkSize, overlap } = options;
  const chunks: string[] = [];
  const sentences = text.split(/(?<=[.!?])\s+/);
  let current = "";

  for (const sentence of sentences) {
    if (current.length + sentence.length > chunkSize && current.length > 0) {
      chunks.push(current.trim());
      const words = current.split(" ");
      const overlapWords = words.slice(-Math.floor(overlap / 5));
      current = overlapWords.join(" ") + " " + sentence;
    } else {
      current += (current ? " " : "") + sentence;
    }
  }

  if (current.trim()) chunks.push(current.trim());
  return chunks;
}

Cost Optimization

function selectModel(task: TaskType): string {
  switch (task) {
    case "classification":
    case "extraction":
      return "claude-haiku-4-20250514";
    case "analysis":
    case "coding":
      return "claude-sonnet-4-20250514";
    case "complex-reasoning":
      return "claude-opus-4-5-20251101";
    default:
      return "claude-sonnet-4-20250514";
  }
}

Use the smallest model that achieves acceptable quality. Cache embeddings and responses where possible. Batch requests when latency is not critical.

Anti-Patterns

Sending entire documents when only relevant chunks are needed
Not implementing retry logic with exponential backoff for API calls
Ignoring token usage tracking (leads to unexpected costs)
Using the most expensive model for simple classification tasks
Not validating or sanitizing LLM output before using it in code
Building RAG without evaluating retrieval quality first

Checklist

API calls wrapped with retry logic and error handling
Streaming used for user-facing responses
Function calling schemas include clear descriptions
RAG chunks sized appropriately (500-1000 tokens) with overlap
Model selection based on task complexity
Token usage tracked and monitored for cost control
LLM output validated before downstream use
Embeddings cached to avoid redundant API calls

llm-integration

LLM Integration

API Client Pattern