Blogs on 'llm apis' tag
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How Should I Normalize Tool Calls Across OpenAI, Claude, Grok, and DeepSeek?
How can an application handle tool calls consistently across OpenAI, Claude, Grok, and DeepSeek? This question explores shared interfaces, provider differences, validation, and tool execution.
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How Should My App Handle an AI Model That Refuses a Harmless Request?
When one AI provider refuses a request another model answers, how should an app respond? This question explores refusal handling, retries, user feedback, and differences between provider APIs.
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Is Dynamic LLM Routing Worth the Complexity?
When an app can call several LLM providers, should each request be routed to a different model? Explore the tradeoffs around task classification, latency, fallbacks, and measuring whether routing helps.
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What Should My App Do When a User Disconnects During an LLM Response?
When a user leaves during a streamed LLM response, should your app cancel the model request or keep it running? Explore disconnect handling, partial answers, and provider-side cancellation.
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Can I Send the Same Prompt to Two LLM Providers and Use the First Response?
Would sending one prompt to two AI providers improve response time, or introduce hidden costs and reliability risks? Explore hedged requests, cancellation, streaming, tools, and data handling.
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What’s the Right Way to Handle Rate Limits in a Multi-Provider LLM Integration?
When an application calls several LLM APIs, how should it respond to 429 errors without creating retry storms or unnecessary delays? Explore rate limits, queues, retries, and provider fallback.
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How Can I Keep LLM API Costs Predictable When Usage Spikes?
What happens to an AI feature’s API bill when usage surges? This question looks at token usage, rate limits, retries, fallbacks, and the safeguards worth considering.
