Why basic RAG fails at multi-hop reasoning (and how GraphRAG fixes it)
The current approach to designing LLMs within AI engineering is oversimplified. According to the echo chamber’s view, solving LLM hallucinatio…
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Stop the token bleed: building token-efficient multi-agent systems
Every engineering team deploying AI agents eventually discovers an uncomfortable truth: the model isn’t the biggest expense. The hidden cost T…
Why your AI pipeline costs 10x more after the demo
Every token has a price. The problem is that most AI systems don’t reveal the bill until they reach production. The post Why your AI pipeline costs…
Say goodbye to K8s GPU pain: How DRA changes everything
Consider a platform team managing a shared GPU cluster with a mix of B200s, H100s, and recently added B300s. Every The post Say goodbye to K8s GPU p…
When do AI agents need permission boundaries?
An AI agent feels harmless when it only produces text, but the risk profile changes the moment it calls a The post When do AI agents need permission…
Can prompt caching tame RAG costs without sacrificing accuracy?
The AI ecosystem is drowning in tutorials on how to build a retrieval-augmented generation (RAG) app in five minutes. The The post Can prompt cachin…
Why smarter AI caching sometimes makes everything slower
Caching was one of the most critical optimizations in modern AI systems long before most teams realized it. Early prototypes The post Why smarter AI…
The “silent hallucination” loop: how our autonomous data pipeline poisoned its own vector store
Nothing matches the dread of checking a perfectly green observability dashboard with sub-100ms latency, right before an enterprise client emails The…
Why traditional CI/CD fails for LLMs (and the release gates we built to fix it)
This article explains why traditional CI/CD gates are not enough for production AI systems. I share a practical release-gating approach The post Why…
Template-based data extraction is dead. Here’s what comes next.
Modern businesses are in a constant, uphill battle against what to do with unstructured data: PDFs, contracts, scanned images, customer The post Tem…
Your AI pipeline is broken, and your dashboards don’t know it
Recently, a crucial RAG pipeline used by one of our corporate clients began hallucinating about financial numbers without notifying us The post Your…
Beyond the stack trace: why AI requires a new debugging paradigm
For some time, debugging has relied on the assumption that software is deterministic. It’s expected that with the same input, The post Beyond…
From Jupyter Notebook to production: How to ship AI systems that actually work
Moving from experimentation to production in AI requires a transformation of mindset, architecture, and engineering discipline. There’s no API…
Debugging the undebuggable: building observability into probabilistic AI systems
Debugging used to be straightforward: A service failed, you checked the logs, followed the stack trace, and fixed the bug. The post Debugging the un…
How to build your first end-to-end AI workflow in n8n
n8n is one of the most powerful workflow automation platforms available today, and the fastest way to learn it is The post How to build your first e…
The Rust sidecar pattern that fixes Python AI’s biggest weakness
In the world of AI, “it works on my machine” is the most dangerous phrase a developer can muster. Transitioning The post The Rust sidecar pattern th…
How AI-native systems are built
The core problem: the “brittle” enterprise Traditional enterprise software is deterministic; composed of a series of “If-Then” statements that break…
A nine-point checklist for shipping production-ready AI
Starting from square one Most teams can build an AI prototype. A notebook answers a few prompts, a demo agent The post A nine-point checklist for sh…