Practical knowledge for running LLMs in production: RAG pipelines, prompt design, inference cost reduction, evaluation, and hallucination mitigation.
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.
Learn how to cache AI responses using semantic similarity to reduce LLM calls for duplicate queries. Covers Semantic Cache integration with AI Gateway and cost savings estimates.
Learn how fine-tuning works from the basics. Covers PEFT/LoRA, when to use RAG, cost estimates, and a checklist for B2B companies evaluating custom model development.
AI grounding anchors LLM responses to authoritative sources. Learn how RAG and web search reduce hallucinations, plus implementation steps for enterprise deployment.
AI Gateway is a relay layer managing multiple LLM providers via a unified interface. A design and operations guide for implementing cost optimization, failover, and audit logging.
Synthetic Test evaluates AI using synthetic data. We explain its role in LLM & AI agent quality assurance, differences from LLM-as-a-Judge, and 4 implementation steps.
Learn how Test-Time Compute scaling works and how to optimize the tradeoff between inference cost and accuracy. A practical guide to LLM operations and cost design in the age of reasoning models.
Explore hybrid AI design combining cloud LLM and on-device SLM, with strategies for routing tasks by cost, latency, and compliance.
Protect LLM apps from prompt injection & hallucinations. Learn guardrail design basics, input/output guards, evaluation, and multi-tenant operations from an implementation perspective.
Learn the basics of edge AI & on-device LLMs, and how to design workflows requiring low latency, data privacy, or unstable connectivity—where cloud LLMs fall short.
