Engineering Insights
Insights, patterns, and production experience from our engineering team, no fluff, just real-world systems thinking.
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Why "choose an LLM, write a prompt, ship it" works for a demo - and what actually breaks when real users arrive. Lessons on distributed systems, reliability, and production architecture.
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Why code should decide which tools to call, not LLMs. Deterministic tool calling vs agentic AI for money-critical decisions. Production refund system architecture.
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Why "choose an LLM, write a prompt, ship it" works for a demo - and what breaks when real users arrive. Distributed systems, reliability, and production architecture.
Read More about AI Engineering Is Not Just Prompts and LLMs
Why LLM-generated curricula need a deterministic validation graph, not a longer prompt: topological sort, cycle detection, and the repair-loop architecture behind our EdTech platform's course generator.
Read More about Why We Don't Let AI Design Your Curriculum Alone
When a correct rate limiter becomes an incorrect architecture. How shared state, Redis, and layered edge protection solve the hardest distributed systems problems.
Read More about Distributed Rate Limiting in Spring Cloud Gateway: Lessons from Production
Gateway-centric, distributed, or internal tokens? A practical guide to JWT trust boundaries with real-world tradeoffs across four production patterns.
Read More about Where Should JWT Validation Happen in a Microservices Architecture?
This is a situation many organizations encounter as they scale real-time data platforms. While synchronization technologies can move millions of records efficiently, business stakeholders ultimately care about one thing: whether the search results are correct. Here's how we approached that challenge using PostgreSQL, Kafka Connect, and OpenSearch.
Read More about The search platform was healthy. The dashboards were green. Yet users were reporting missing results.
Inside the process behind a production-grade RAG system: quantitative evaluation, hybrid retrieval, reranking, and on-prem data governance.
Read More about How We Engineer AI Assistants That Employees Actually Trust
Part 1 of our RAG engineering series: hybrid retrieval, reciprocal rank fusion, cross-encoder reranking, and the roadmap that followed.
Read More about Building a Production-Ready RAG System: From Retrieval to Reliable Answers
Latency spikes, timeouts and throttled-but-idle capacity are usually not capacity problems. A CTO's guide to workload isolation, container-aware pool sizing and backpressure.
Read More about Your Cloud Bill Is Often an Architecture Problem
Practical lessons from building Angular applications in production: signals, readonly semantics, and component architecture.
Read More about When Angular Signals Actually Improve Performance