#ai
17 articles
Model Context Protocol (MCP) and Tool-Use Infrastructure
How LLMs safely reach the outside world — from raw function calling to MCP, the open standard that collapses N×M bespoke integrations to N+M, with production-grade security, reliability, and a ~88% token reduction via deferred tool loading.
Design an LLM Observability Platform
Build the distributed tracing backbone for non-deterministic, multi-step LLM applications — capturing every prompt, completion, token count, and dollar cost across chains, retrievals, and tool calls so you can debug a failed agent run and account for every cent.
Design an LLM Gateway (AI Gateway & Model Router)
A single proxy control plane in front of OpenAI, Anthropic, Google, and open models — routing ~65 trillion tokens a month with automatic failover, semantic caching, per-team budget enforcement, and streaming SSE passthrough, all under 50 ms of added latency.
Design an LLM Fine-Tuning Platform
Turn a base model and a dataset into a deployed fine-tuned adapter at scale — the end-to-end platform covering dataset ingestion, LoRA/QLoRA/DPO training, fault-tolerant distributed GPU scheduling, eval gating, and multi-LoRA serving for hundreds of concurrent fine-tunes.
Design an LLM Evaluation Platform
Build the system that tells a team whether a prompt or model change made the product better or worse — automatically. Covers offline eval with LLM-as-judge, CI regression gating, online production sampling, human annotation queues, and eval for RAG, agents, and classifiers at the scale of 450 million evaluations per month.
Design a GraphRAG System (Knowledge-Graph-Augmented Retrieval)
When vanilla vector RAG fails on "summarize the entire corpus" and multi-hop questions, you build a knowledge graph first — covering entity extraction, Leiden community detection, map-reduce global search, and graph traversal for multi-hop, based on Microsoft GraphRAG and production deployments at Neo4j, LinkedIn, and Writer.
Design a Feature Store
Serve the exact same feature values to model training and online inference — eliminating training-serving skew — across batch, streaming, and on-demand tiers at sub-10ms latency and millions of reads per second. The architecture powering Uber Michelangelo, Airbnb Chronon, and DoorDash Gigascale.
Design an Intelligent Document Processing Pipeline
Turn millions of messy PDFs, scans, and invoices into validated structured JSON at scale — the end-to-end pipeline covering OCR, layout analysis, LLM-based field extraction, confidence-scored routing, human-in-the-loop review, and the cost math that determines build-vs-buy.
Design a Customer-Support AI Assistant
Architect a production customer-support AI that deflects 60–80% of tickets by combining RAG over a help center, real-action tools (refunds, cancellations, account changes), per-session memory, guardrails, and a structured handoff to a human agent — all while keeping hallucination below 2%.
Design a Realtime Voice AI Agent
Build a full-duplex voice agent that answers PSTN and WebRTC calls with sub-800ms voice-to-voice latency — covering the cascade pipeline (STT→LLM→TTS), turn detection, barge-in, telephony transport, and scaling to thousands of simultaneous calls.
Design an AI Guardrails & Safety System
Build the validation layer that wraps every LLM call — detecting prompt injections, redacting PII, catching toxic outputs, and verifying groundedness — while staying inside a 200 ms latency budget for 10 million daily requests.
Design an AI Coding Assistant (Copilot / Cursor)
Architect a system that delivers inline ghost-text completions in under 200ms and drives an autonomous agent that edits dozens of files — the two-product architecture behind GitHub Copilot, Cursor, and Sourcegraph Cody at billions of completions per day.
The ML / GenAI System Design Interview Framework
A 7-step, 45-minute framework for ML and GenAI system design rounds — covering the data/feedback loop, the candidate-generation + ranking funnel, and the GenAI decision tree from prompt-engineering to fine-tune, with a hire vs no-hire signals table.
Design a Vector Database / Semantic Search Service
Index 1 billion 768-dimensional vectors and answer top-k similarity queries in under 20 ms — the ANN indexing, sharding, and filtering architecture behind Pinecone, Weaviate, and pgvector.
Design a RAG (Retrieval-Augmented Generation) Pipeline
Ground an LLM in 10 million documents (50 million chunks) with sub-2-second answers and a hallucination rate measurable by automated eval — the end-to-end ingestion, retrieval, reranking, and generation pipeline powering enterprise knowledge assistants.
Design an LLM Inference & Serving System
Serve token generation for a 70B-parameter model at scale — where KV cache, not FLOPs, caps concurrency and continuous batching is what separates good GPU utilization from terrible utilization.
Design an AI Agent Platform
Build a platform that runs autonomous LLM agents — each capable of planning, calling tools, and completing multi-step tasks lasting minutes to hours — with durable state, idempotent tool execution, and per-tenant safety guardrails.