Step-by-step articles on LLM APIs & Prompting, part of our Generative AI collection.
Showing 10 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...
The system prompt is the brief you give the model before the conversation starts. Most poor LLM results come from a vague brief, not a weak model. 1. Give context, not just commands Explain who the users are and why the task matters. A model that understands t...
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...
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...
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...
Few-shot prompting means showing the model a handful of input/output examples before the real input. It is the fastest way to teach a specific style or labelling scheme without fine-tuning. A classification example Label each commit message as feat, fix, docs...
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'...
