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Using the `response_format: {type: 'json_schema'}` parameter (OpenAI) or XML tags with output parsers (Claude) ensures the model always returns valid structured data. This eliminates regex hacks, reduces prompt length, and makes your pipelines far more reliable.

Your system prompt is sent with every API request and billed as input tokens every time. A 2,000-token system prompt on 100,000 daily calls equals 200M tokens/day just in system prompts. Keep system prompts concise, or use prompt caching to amortize the cost.

Setting temperature=0 reduces randomness but doesn't guarantee identical outputs across API calls - models can still vary due to sampling hardware differences. For true reproducibility, use temperature=0 AND set a fixed seed parameter (available in OpenAI and some other APIs).

Always set max_tokens in your API calls to prevent runaway generation. A missing token limit on a recursive agent loop can generate tens of thousands of tokens in seconds, leading to unexpected bills.

When retrying failed LLM API calls, use exponential backoff with random jitter rather than fixed delays. This prevents thundering herd problems when multiple clients retry simultaneously after a provider outage.

Enable streaming (stream: true) for any generation over 500 tokens. Without streaming, the user waits for the entire response to complete before seeing anything. With streaming, the first tokens appear in under a second, dramatically improving perceived performance.

The cost of LLM API calls has fallen dramatically since 2023. GPT-3.5-Turbo launched at $2/M input tokens; by 2026, comparable-quality models like Gemini Flash and GPT-4o-mini cost under $0.15/M input tokens — a reduction of over 90% in under three years.

Many LLM APIs allow you to specify which fields you want in the response. Only request the data you need (e.g., just the generated text, not full logprobs if unused) to reduce data transfer and processing.

When making API calls to LLMs, implement exponential backoff for retries. This helps manage rate limits and temporary server issues gracefully without overwhelming the API.

Implement caching strategies for repeated queries to reduce API calls and costs.

If you have many small, independent tasks for an LLM, batch them into a single API call if the model and API support it, rather than many separate calls. This can reduce overhead.

Be aware of API rate limits (requests per minute/day). Design your application to handle these gracefully, perhaps with retries and exponential backoff.

Never embed API keys directly in client-side code or public repositories. Use environment variables or secure secret management services.

For applications like chatbots, use streaming API responses if available. This allows text to be displayed token by token as it's generated, improving perceived responsiveness.