Insights
Architecture decisions, trade-offs and lessons from shipping RAG platforms, agents and LLM products for real users.
Latest3 min read
Which legal AI use cases deliver value today, why generic chatbots fail in legal work, and the requirements (citations, confidentiality, access control, anonymization and evaluation) that make legal AI trustworthy.
How LangGraph and CrewAI differ in mental model, control, state, human-in-the-loop and observability, when each one fits, and why many production systems use ideas from both.
A practical breakdown of what makes a RAG project cheap or expensive to build and to run: data complexity, integrations, access control, evaluation, embeddings, vector storage and per-query token costs, with the math to estimate your own.
A practical decision framework for choosing between deterministic workflow automation, AI-enhanced workflows and autonomous agents, with cost, reliability and maintenance trade-offs.
The decisions that separate a RAG demo from a production system: evaluation, ingestion, hybrid retrieval, access control, PII, citations, cost and observability, as a checklist you can use on your own project.
A plain-English guide to the Model Context Protocol: how MCP servers expose tools and data to AI agents, when it beats hand-built integrations, and the security controls a production rollout needs.
Lessons from shipping a 24/7 WhatsApp voice pipeline — Whisper transcription, Claude-generated replies, and the prompt-caching setup that cut input-token costs by roughly 90%.
How I designed an agent platform on LangGraph's StateGraph with Model Context Protocol tool access, persistent checkpointed memory, and human approval steps before sensitive actions.
A practical breakdown of when RAG outperforms fine-tuning and vice versa, based on real production experience building both types of systems.
How I architected a production RAG system processing 50,000+ French legal documents using Claude Sonnet and Pinecone with 95%+ retrieval confidence.
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