Introduction: LLM API calls are expensive and slow. A single GPT-4 request can cost cents and take seconds—multiply that by thousands of users asking similar questions, and costs spiral quickly. Semantic caching solves this by recognizing that “What’s the weather in NYC?” and “Tell me NYC weather” are essentially the same query. Instead of exact […]
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Google Gemini API: Building Multimodal AI Applications with 2M Token Context
Introduction: Google’s Gemini API represents a significant leap in multimodal AI capabilities. Launched in December 2023, Gemini models are natively multimodal, trained from the ground up to understand and generate text, images, audio, and video. With context windows up to 2 million tokens and native Google Search grounding, Gemini offers unique capabilities for building sophisticated […]
Read more →Serverless AI Architecture: Building Scalable LLM Applications
Three years ago, I built my first serverless LLM application. It failed spectacularly. Cold starts made responses take 15 seconds. Timeouts killed long-running requests. Costs spiraled out of control. After architecting 30+ serverless AI systems, I’ve learned what works. Here’s the complete guide to building scalable serverless LLM applications. Figure 1: Serverless AI Architecture Overview […]
Read more →Tips and Tricks – Implement Retry Logic for LLM API Calls
Handle rate limits and transient failures gracefully with exponential backoff.
Read more →Structured Output Generation: Reliable JSON from Language Models
Introduction: LLMs generate text, but applications need structured data—JSON objects, database records, API payloads. Getting reliable structured output from language models requires more than asking nicely in the prompt. This guide covers practical techniques for structured generation: defining schemas with Pydantic or JSON Schema, using constrained decoding to guarantee valid output, implementing retry logic with […]
Read more →LLM Cost Optimization: Model Routing, Token Reduction, and Budget Management (Part 2 of 2)
Introduction: LLM API costs can escalate quickly—a single GPT-4 call costs 100x more than GPT-4o-mini for the same tokens. Effective cost optimization requires a multi-pronged approach: intelligent model routing based on task complexity, aggressive caching for repeated queries, prompt optimization to reduce token usage, and batching to maximize throughput. This guide covers practical cost optimization […]
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