Practical knowledge for running LLMs in production: RAG pipelines, prompt design, inference cost reduction, evaluation, and hallucination mitigation.
Learn how to build a lightweight AI inference environment using Firecracker. This guide covers running LLMs in microVMs, plus implementation steps, scaling strategies, and best practices to optimize inference costs.
Explains converged database implementation patterns for unifying LLM inference logs across multiple storages, improving anomaly detection in production. Covers unified I/O, latency, token usage & error tracking.
Learn how EDD integrates evaluation metrics throughout the entire cycle—not gut feeling—plus practical steps to auto-tune prompts and parameters.
Learn how SLM distillation uses large LLMs as teachers to build cost-effective, task-specific private models—covering the mechanism and implementation steps.
Learn patterns & implementation steps for multi-tenant cache design that securely shares prompt context across tenants, significantly reducing inference costs.
Learn how LNNs dynamically adapt time-constants during inference, how they differ from traditional models, and their edge AI potential—all while keeping trained weights fixed.
Beyond single LLM limits: compare routing layer mechanisms that dynamically assign optimal models and templates based on query content, with implementation patterns.
Learn how to enforce LLM outputs with JSON Schema to prevent parse failures and downtime. From fundamentals to practical type-safe design for B2B workflow automation.
Learn how "token traps" cause billing spikes in high-frequency agent loops, and how to prevent cost explosions with budget caps, throttling, and smart loop design.
Explore how "thinking time" causes response delays in multi-step reasoning agents, and compare latency budget allocation strategies and implementation patterns based on task complexity.
Explore Model Merging: mathematically combine fine-tuned models to integrate multiple skills while reducing computational costs.
Learn how Adaptive RAG eliminates inefficiencies of traditional RAG by selectively retrieving only when needed—covering mechanisms, implementation patterns, and B2B use cases.
