What's the first step in production deployment? How do you evaluate readiness?
Useful self-assessment across five dimensions: Security (API Key in env vars, different keys per environment, spending limits set), Reliability (retry logic, timeouts, fallbacks), Cost Control (Context Window limits, Prompt Caching enabled, token usage logged), Observability (key metrics logged, cost alerts, user tracing), Scalability (Rate Limit handling for high concurrency, queue mechanism).
If all five are covered, you have basic production safeguards. For any gaps, fix the highest-impact ones first.
How do you use Batch API to dramatically reduce batch processing costs?
Anthropic's Batch API is 50% cheaper than standard API, but doesn't guarantee immediate response (typically within 24 hours). Ideal for non-real-time batch tasks.
Usage: package multiple requests into JSONL (one per line); submit for a batch_id; periodically poll status; download results when complete.
Cost estimate: Batch API costs 50% of standard. For 100K+ monthly batch requests combined with Prompt Caching, total costs can drop to 10-15% of standard real-time API.
Suitable: background tasks, offline content generation, high-volume homogeneous batch analysis. Not suitable: any real-time user-waiting interactions.
What's the correct Streaming implementation and when do you need it?
Streaming pushes output to your application every few tokens as Claude generates, rather than waiting for complete generation. For waiting-user scenarios, Streaming lets users see text appearing word by word.
When to use: user-waiting scenarios (chat interfaces, long content generation); generating longer content (over 200-300 words); strict UX requirements for real-time feel.
When not to use: background batch processing; very short outputs (under 50 tokens); need complete output before processing.
Implementation: handle each message_delta event and accumulate fragments; handle mid-stream interruptions; Python SDK's with client.messages.stream() context manager is the cleanest approach.
How do you design testing strategy for Claude API applications?
AI application testing is more complex because LLM output is non-deterministic — can't use exact output matching.
Functional Testing: test whether output meets requirements — length in range, required structure present, no prohibited content. Use semantic similarity or LLM-as-Judge.
Regression Testing: maintain a golden test set with expected output directions. Use LLM to evaluate whether changes improve or degrade output.
Cost and Performance Testing: measure average token consumption, P95 latency, error rate per scenario as baselines.
Tools: Anthropic Workbench for prompt iteration; Pytest with Anthropic SDK for automation; Ragas for RAG quality evaluation.
Getting an API example to run and running an API stably in production are completely different things. Many developers test Claude API smoothly on localhost, then hit unexpected problems in production — Rate Limits, exploding token costs, Context Window management issues, no Observability to know where things went wrong.
Never write API Keys in code. Use environment variables or cloud secrets management services. Use different API Keys for different environments with separate spending limits in console.anthropic.com.
When API returns 429, don't immediately retry. Use Exponential Backoff with Jitter: wait 1 second after first failure, 2 seconds after second, 4 after third, max 5 retries.
Set a maximum conversation history limit (e.g., last 10 turns or 100K tokens total). Use sliding window to discard oldest conversations when exceeded. Log token counts from the usage field every call.
If System Prompt exceeds 1,024 tokens, add cache_control: {type: ephemeral} to reduce that portion's cost by 90%. Check usage.cache_read_input_tokens to confirm cache is hitting.
429: exponential backoff retry. 500/529: retry once then return friendly error message, log the error. 400: don't retry, log detailed error for debugging. Timeout: enable Streaming with 60-120 second timeout.
Log every API call: timestamp, model, input/output token counts, latency, error type, user ID. Build metrics: average latency, daily costs, error rate, P99 latency. Set alerts: daily cost threshold, error rate above 5%, latency spikes.