Category: Artificial Intelligence
Artificial Intelligence
Semantic Caching for LLM APIs: The Engineer’s Guide
Semantic caching lets an application answer an incoming LLM query by returning a previously generated response when the new question is semantically close to a cached one — no model call at all. The...
Artificial Intelligence
Speculative Decoding in vLLM: The Engineer’s Setup
Speculative decoding lets a large language model emit tokens faster by drafting candidates with a small, cheap model and verifying them in a single forward pass of the target. Google researchers measured a 2X-3X...
Artificial Intelligence
LLM Tracing and Evaluation That Works in Production
You make LLM tracing and evaluation pay off in production by treating the trace — not the HTTP request, and not the prompt — as the unit of quality. Emit one instrumented span per...
Artificial Intelligence
API Key Rotation for AI APIs: The Zero-Downtime Runbook
API key rotation for AI providers only stays downtime-free when you treat it as a two-key window: point every client at one key, regenerate the idle second key, migrate clients across to it, then...
Artificial Intelligence
Model Deprecation Windows: Planning for API Retirements
Model deprecation windows are the notice periods a provider gives you before a model stops answering API requests, and they are the difference between a planned migration and a production outage. OpenAI commits to...
Artificial Intelligence
Offline training: how OpenAI air-gaps frontier RL runs
OpenAI’s security post on its frontier training environment confirms a major architectural shift: reinforcement learning for frontier models now runs with no internet access, against a pre-stored mirror of the web. The trigger was...
Artificial Intelligence
VPC Endpoints for AI APIs: The Private Access Setup
Yes — you can call Bedrock and Azure OpenAI with zero packets crossing the public internet. On AWS, a VPC endpoint does the job: interface endpoints keep Amazon Bedrock traffic inside the AWS backbone...
Artificial Intelligence
Structured Outputs: Getting Reliable JSON From LLMs
Structured outputs solve the most common integration failure in production LLM features: the model returns prose, truncated JSON, or a payload that parses but does not match the contract your code expects. Schema-constrained decoding...
Artificial Intelligence
EU Data Residency for AI Workloads: The Setup Guide
Keeping AI workloads inside the EU is a routing decision, not a hosting decision. On all three hyperscalers the default path for foundation models is global, and the residency guarantee arrives only when you...
Artificial Intelligence
Zero Data Retention: Auditing the Defaults You Inherit
Zero data retention (ZDR) means a provider keeps no copy of your prompts, completions or derived state after a request finishes. It is a configuration and procurement target, not a product tier you can...
Artificial Intelligence
Provisioned Throughput: Sizing Reserved AI Capacity
Provisioned throughput is a reservation against model-serving capacity: instead of paying per token on shared infrastructure, you pay a fixed rate for dedicated processing capacity held around the clock. The unit differs by provider...
Artificial Intelligence
Half-Price Tokens: Batch Inference for Async Workloads
Batch inference APIs sell you the same models at half price in exchange for latency you can budget for: results arrive in minutes or hours instead of milliseconds. Anthropic charges all batch usage at...