Guide
Eval pipelines for LLM features — what they are and how to build one
A practical guide to golden sets, property-based scoring, and CI gates — so prompt and retrieval changes do not silently break production copilots.
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integration
8 guides on integration — architecture patterns, rollout strategy, and production integration for engineering teams.
Guide
Prompt injection and LLM security for SaaS
A practical security guide for multi-tenant products — why system prompts are not enough, where attacks actually land, and the integration patterns that hold up in production.
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Langfuse for LLM observability — where it fits in your middleware stack
How to trace model calls, debug prompts, and run evals with Langfuse — integrated into server-side LLM middleware, not bolted onto a frontend demo.
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LLM middleware: what it is, why you need it, and how to implement it
A practical guide to the server-side layer between your app and the model — auth, rate limits, routing, logging, and the patterns that keep AI features production-ready.
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Build an agent with LangChain — a practical tutorial
Step-by-step guide to building a tool-calling agent with LangChain and LangGraph, from first prototype to patterns that survive production.
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When not to use RAG
RAG is the default answer for every AI feature — but often the wrong one. A decision guide for engineering leaders scoping retrieval, tools, and middleware.
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AI integration services — what we build and how we deliver
A practical overview of 475 Cumulus capabilities, engagement phases, and how we integrate LLM features into existing products without a platform rewrite.
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What production-ready LLM integration actually means
A practical checklist for engineering leaders — beyond the demo and before you call an AI feature shipped.
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