Building an AI application with Claude that works reliably in production requires more than calling the API and displaying the response. Here are the patterns that matter for production deployments.
System Prompt Architecture
The system prompt is the most important lever for shaping Claude’s behaviour in your application. Key principles: be specific, not vague — “you are a helpful assistant” produces generic behaviour; “you are a customer support agent for Acme Corp, you help customers with order status, returns, and product questions, you never discuss competitors, and when you cannot help you route to human support” produces reliable, bounded behaviour. Separate persona, context, and constraints: persona (who Claude is in this context), context (what information it has access to), and constraints (what it must not do) are logically distinct and should be organised clearly in the system prompt. Use the `
Handling Long Context and Retrieval
Claude’s 200,000-token context window is large, but filling it entirely creates latency and cost. The pattern: use the context window for genuinely relevant material, not as a dump of everything potentially useful. Structured retrieval: if you are building a RAG application, chunk documents carefully — 200–500 token chunks with meaningful boundaries (sentence or paragraph level, not mid-sentence) and 10–20% overlap between chunks to avoid cutting important context. Reranking: retrieve more candidates than you need (top 20), then rerank them semantically (Cohere Rerank, or an LLM judge) to select the top 5–8 for the actual prompt. This significantly improves relevance precision. Citation and grounding: if Claude is answering based on retrieved documents, instruct it explicitly to cite sources and to answer “I don’t know based on the provided information” when the answer is not in the retrieved documents. This reduces hallucination rates dramatically for grounded applications. Conversation history management: for multi-turn conversations, keep full history in the context while it fits; once it approaches the context limit, summarise older turns rather than truncating them — losing important context from earlier in a conversation degrades quality.
Reliability and Error Handling
Structured output: for applications that process Claude’s output programmatically (JSON, structured data), use Claude’s native JSON mode or instruct explicitly with a JSON schema. Add output validation that retries on parse failure — a retry with “your previous response was not valid JSON, please respond with only a valid JSON object” recovers most failures. Streaming: use the streaming API for user-facing interactions — displaying text as it streams dramatically improves perceived responsiveness. Rate limits and retries: implement exponential backoff for API errors — the Claude API has per-minute and per-day limits. Use a rate limiter in your application layer. Cost management: token usage compounds at scale. Monitor input + output tokens per request in your observability stack. Long system prompts + long retrieved context + long conversations compound costs. Prompt caching (for stable system prompts) reduces costs by ~90% on the system prompt tokens for repeated requests. Observability: log every API request with its system prompt hash, input, output, latency, and token usage. Without this, you cannot debug production failures or track regression after prompt changes.
