Introduction: LLM APIs have strict rate limits—requests per minute, tokens per minute, and concurrent request caps. Hit these limits and your application grinds to a halt with 429 errors. Worse, aggressive retry logic can trigger longer cooldowns. Proper rate limiting isn’t just about staying under limits; it’s about maximizing throughput while gracefully handling bursts, prioritizing […]
Read more →Category: Technology Engineering
Technology Engineering
Multi-Modal LLM Integration: Building Applications with Vision Capabilities
Introduction: Modern LLMs understand more than text. GPT-4V, Claude 3, and Gemini can process images alongside text, enabling applications that reason across modalities. Building multi-modal applications requires handling image encoding, managing mixed-content prompts, and designing interactions that leverage visual understanding. This guide covers practical patterns for integrating vision capabilities: encoding images for API calls, building […]
Read more →DevSecOps: Integrating Security into DevOps – Part 6
Continuing from my previous blog, let’s explore some more advanced topics related to DevSecOps implementation. Threat Intelligence Threat intelligence is the process of gathering information about potential threats and vulnerabilities to an organization’s systems and applications. It involves collecting, analyzing, and disseminating information about potential threats, vulnerabilities, and threat actors. Threat intelligence includes the following […]
Read more →LLM Request Batching: Maximizing Throughput with Parallel Processing
Introduction: Processing LLM requests one at a time is inefficient. When you have multiple independent requests, sequential processing wastes time waiting for each response before starting the next. Batching groups requests together for parallel processing, dramatically improving throughput. But batching LLM requests isn’t straightforward—you need to handle rate limits, manage concurrent connections, deal with partial […]
Read more →LLM Error Handling: Building Resilient AI Applications
Introduction: LLM APIs fail. Rate limits get hit, servers time out, responses get truncated, and models occasionally return garbage. Production applications need robust error handling that gracefully recovers from failures without losing user context or corrupting state. This guide covers practical error handling strategies: detecting and classifying different error types, implementing retry logic with exponential […]
Read more →Streaming Response Patterns: Building Responsive LLM Applications
Introduction: Waiting for complete LLM responses creates poor user experiences. Users stare at loading spinners while models generate hundreds of tokens. Streaming delivers tokens as they’re generated, showing users immediate progress and reducing perceived latency dramatically. But streaming introduces complexity: you need to handle partial responses, buffer tokens for processing, manage connection failures mid-stream, and […]
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