Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Reduce your Claude API costs by 60-90% with these eight production-tested strategies. Covers prompt caching, model tiering, token budgeting, batch processing, response caching, context compression, and more.
A technical deep dive into how modern LLM tokenizers work, the tradeoffs between BPE and SentencePiece, and emerging approaches that improve multilingual and code performance.
Compare CallSphere and PolyAI for AI voice agents. See features, pricing, compliance, and which platform is better for your business.
Where contextual retrieval RAG is heading on Claude — agentic memory, long-context tradeoffs, self-improving indexes, MCP — and how to prepare today.
The metrics that prove contextual retrieval RAG works on Claude — recall@k, context faithfulness, LLM-judge correctness, and live production signals.
An end-to-end walkthrough of shipping contextual retrieval RAG on Claude — eval set, contextual chunks, hybrid search, and a confidence-gated launch.
Failure modes, blast radius, and containment controls for contextual retrieval RAG on Claude — provenance, faithfulness checks, and scoped agent autonomy.
The roles, skills, and hiring shifts teams need to ship contextual retrieval RAG on Claude — from eval owners to MCP connector reliability.
Scale Contextual Retrieval for agentic RAG from one team to many: shared indexes, ingestion contracts, canonical prompts, and central evals with Claude.
