GenAI System Design
Practical notes on building and scaling LLM systems in production.
This blog covers the practical side of building and scaling GenAI systems in production. From inference optimization and memory management to retrieval pipelines and serving infrastructure, the focus is on how these systems actually work at scale, the trade-offs involved, and the decisions engineers face when putting them into production.
Recent Posts
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Case Study: Designing a GitHub Copilot-Style Code Completion Backend
A Staff+ GenAI system design case study. How do you build code autocomplete at 1B tokens per day, under 100ms p99 latency, across millions of developers?
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Attention Mechanisms: A Backend Engineer's Guide
Understanding attention variants (MHA, MQA, GQA, SWA) is not just an ML topic. The variant your model uses determines your KV cache budget, GPU tier, tensor parallelism constraints, and maximum batch size.
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Case Study: Designing an AI-Powered Order Support Agent for an Enterprise Logistics Platform
A production deep dive into multi-agent orchestration, hybrid retrieval, inference framework tradeoffs, and why fine-tuning your policy knowledge base will come back to haunt you.
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A Framework for GenAI System Design Case Studies
A 9-step framework for designing production systems around large language models. Covers requirements, architecture choices, data strategy, model selection, inference infrastructure, guardrails, evaluation, and deployment.
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Logits, Sampling, and Token Selection in LLM Inference
Before a token leaves the model, it passes through logit processing and sampling. This step is where temperature, top-k, top-p, and structured output constraints all live. Here's how it works and why it matters for serving.
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