Practical knowledge for running LLMs in production: RAG pipelines, prompt design, inference cost reduction, evaluation, and hallucination mitigation.
Context Engineering is a design approach for dynamically assembling optimal information for LLM apps. Covers differences from prompt engineering, context window components, and RAG/agent implementation patterns.
LLM-as-a-Judge automates LLM output scoring. Covers Pointwise/Pairwise/Reference, bias mitigation, 4 implementation steps, and how it fits with Observability and Guardrails.
Practical guide to cutting LLM production costs. Learn 4 pillars—token optimization, model selection, prompt caching & RAG design—to halve monthly costs without sacrificing accuracy.
From AI observability basics to tracing, evaluation, and cost management for production LLM apps—learn key implementation steps and tool selection tips.
Explains hybrid search using vector search, BM25, and RRF. Practical design patterns and implementation tips to improve RAG system retrieval accuracy.
Fine-tuning vs RAG: which should you choose? We compare both across 4 axes—cost, accuracy, update frequency, and security—to guide your decision by use case.
10 common RAG failures before & after production: chunk design errors, retrieval accuracy drops, hallucination fixes & more. Learn key avoidance strategies before you build.
From basic vector database concepts to comparing top products like Pinecone, Weaviate, and pgvector, plus RAG system integration—a clear guide for AI implementation managers.
Compare open-weight models like GPT OSS, Phi-4, and Llama 4 Scout against cloud APIs on GPU requirements, task accuracy, and TCO. A local AI deployment guide for data sovereignty and cost optimization.
