Every article tagged Claude, in one place.
Showing 16 articles
Large language model APIs all follow the same basic shape: you send a list of messages and get a message back. This guide uses the official Anthropic SDK to call Claude, but the ideas carry over to any provider. 1. Install the SDK and set your key # TypeScript...
Long answers can take many seconds to generate. Streaming shows text as it is produced, so users see progress immediately instead of staring at a spinner. Streaming on the server The SDK's messages.stream() helper gives you an async iterator of events plus a t...
Tool use (also called function calling) lets a model request that your code run a function. The model never runs code itself; it returns a structured request and waits for your result. 1. Describe the tools tools = [ { "name": "search_products", "description":...
Asking a model to "reply in JSON" works most of the time. Production code needs it to work every time. Structured outputs constrain the response to a JSON Schema, so the result always parses. TypeScript with Zod import Anthropic from '@anthropic-ai/sdk'; impor...
The manual agent loop is always the same: call the model, run the requested tools, send results back, repeat. The Anthropic SDKs include a tool runner (currently in beta) that does this for you, with type-checked tool inputs. TypeScript with Zod import Anthrop...
Many apps send the same large prefix on every request: a long system prompt, tool definitions, or a document the user is asking about. Prompt caching stores that prefix so later requests read it from cache at a fraction of the normal input price and with lower...
Once you have an MCP server, connect it to a host app so the model can use it. Here's how to do that in two popular hosts. Claude Desktop Open Settings, then Developer, then Edit Config. This opens claude_desktop_config.json . Add your server under mcpServers...
LLM APIs bill by the token , a chunk of text that is often a word or part of a word. Input and output tokens are priced separately, and output tokens cost several times more than input tokens. Count tokens before you send Token counts depend on the model's tok...
LLM APIs are network services under heavy load, so failures are normal. The trick is knowing which errors to retry and which to surface to the user. Retryable vs non-retryable Retry: 429 rate limit, 5xx server errors, overloaded errors, timeouts and dropped co...
Users trust an answer more when they can click through to the source. Citations also make hallucinations easy to spot: an unsupported claim has nothing to point at. Option 1: Ask for chunk IDs Answer the question using only the sources below. After each senten...
Host apps like Claude Desktop include an MCP client, but you can embed one in your own app too. That lets your product use any MCP server as a tool provider. Connect and list tools import { Client } from '@modelcontextprotocol/sdk/client/index.js'; import { St...
Every turn of an agent adds to the conversation: tool calls, large tool results, the model's replies. Long tasks can grow past the context window, and even before that, a bloated context makes the model slower, more expensive and less focused. 1. Keep tool res...
Reasoning models can "think" before answering: they work through a problem internally, then write the final response. Thinking improves results on hard tasks such as multi-step maths, debugging and planning, at the cost of extra tokens and time. Adaptive think...
Modern LLMs read images and PDFs as well as text. That makes it easy to build features like "extract the line items from this invoice" or "explain this error screenshot". Image from a URL const response = await client.messages.create({ model: 'claude-opus-5-5'...
A chunk like "Revenue grew 3% over the previous quarter" is useless on its own: which company, which quarter? Contextual retrieval , a technique published by Anthropic, fixes this by adding a short explanation of where the chunk sits in its document before emb...
If an MCP server is reachable over HTTP, the Claude API can connect to it for you. You list the server in the request and the API handles tool discovery and tool calls, so there's no MCP client or agent loop in your code. The request The MCP connector is a bet...
