Tag

Production

45 articles

AI System DesignWhen Building Gets Cheap, Judgment Gets ExpensiveEight moves for shaping an AI product before the first line of code.August 3, 202620 minAugust 3, 202620 min
Agentic AIA Skill Is a Dependency That Runs CodeAn Agent Skill is not a prompt template. It carries injectable instructions and executable scripts, so treat it like any package you install.August 2, 20268 minAugust 2, 20268 min
From Demo to Product · Part 12Designing UX for a Confidently-Wrong InternTraditional UX assumes the system is correct and fast. An AI feature is neither guaranteed. Design for that.August 2, 20267 minAugust 2, 20267 min
From Demo to Product · Part 11The Reference Architecture for a Production AI AppThe model call is one box out of ten. The other nine are the engineering.August 2, 202611 minAugust 2, 202611 min
From Demo to Product · Part 10Who Owns Quality: The Benevolent Dictator and AI Incident ResponseOn an AI product, quality is a judgment call that needs one owner, and an incident whose postmortem output is a new eval exampleAugust 2, 20269 minAugust 2, 20269 min
From Demo to Product · Part 9Detect, Contain, Monitor: The Risks That Sink AI ProductsYou do not eliminate AI-product risk. You bound the blast radius and watch the rate.August 2, 20267 minAugust 2, 20267 min
From Demo to Product · Part 8The Unit Economics of an AI ProductEvery request costs real money. Why the unit is cost per task, not per call, and how to keep it profitable.August 2, 20268 minAugust 2, 20268 min
From Demo to Product · Part 7LLMOps and the Data FlywheelLaunch is the start of the work. How you operate an AI product, and how production usage compounds into a moat.August 2, 20268 minAugust 2, 20268 min
From Demo to Product · Part 6Ship on Containment, Not PerfectionHow to expose a non-deterministic feature to real users safely, without waiting for a flawlessness that never comesAugust 2, 202612 minAugust 2, 202612 min
From Demo to Product · Part 5Treat the Model as a Flaky, Expensive DependencyThe vendor changes pricing, retires models, and swaps tokenizers under your live product. Wrap it accordingly.July 25, 20265 minJuly 25, 20265 min
From Demo to Product · Part 4Does Your Problem Even Need an LLM?The fit gate to run before you build, and what to do when the answer is noJuly 25, 20265 minJuly 25, 20265 min
From Demo to Product · Part 3Evals Are the Product Discipline (and Writing Them First Backfires)Why eval-driven development done like TDD fails, and what error-analysis-first evaluation looks like insteadJuly 25, 20266 minJuly 25, 20266 min
From Demo to Product · Part 2AI Is Not a MoatThe model is electricity, not the factory. What actually makes an AI product defensible.July 25, 20266 minJuly 25, 20266 min
From Demo to Product · Part 1The Afternoon and the QuarterWhy an AI demo takes an afternoon and an AI product takes a quarter, and what lives in the gapJuly 25, 20265 minJuly 25, 20265 min
Agentic AIWhat Is AgentOps? Operating AI Agents in ProductionHow MLOps and LLMOps extend to autonomous, multi-step agentsJuly 12, 20266 minJuly 12, 20266 min
AI InfrastructureWhat Is OpenTelemetry, and Why It Matters for Agent ObservabilityThe vendor-neutral standard your agent traces should be built onJuly 12, 20266 minJuly 12, 20266 min
AI InfrastructureTracing in Agent Observability: What It Is and Why It MattersTraces, spans, and why a readable trace is the fastest way to debug a misbehaving agentJuly 12, 20267 minJuly 12, 20267 min
LLM IntegrationA Field Guide to Model-Specific PromptingHow prompting actually differs across Claude, GPT, Gemini, Llama, and Mistral, and what transfersJuly 3, 20265 minJuly 3, 20265 min
LLM IntegrationA Real Prompt Engineering Case Study: 65.6 to 91.7 F1 on Job ClassificationWhat a production classification system teaches about which prompt tweaks actually move the needleJuly 3, 20264 minJuly 3, 20264 min
LLM IntegrationGenerating Code with Prompts: The Fundamentals Still MatterComments to code, SQL from schemas, and the verification habit that separates working code from plausible codeJuly 3, 20266 minJuly 3, 20266 min
LLM IntegrationAdversarial Prompting: Injection, Leaking, and JailbreakingThe three attacks that break LLM apps, and the defences that actually hold upJuly 2, 20268 minJuly 2, 20268 min
Prompt Engineering, Properly · Part 14Prompting Reasoning Models Is Almost the Opposite of Prompting Chat ModelsWhy the step-by-step prompts you learned in this series can hurt an o3 or Claude with thinking onJuly 1, 20267 minJuly 1, 20267 min
LLM IntegrationDirectional Stimulus Prompting: Train a Tiny Model to Whisper Hints to a Big OneA small tuneable policy model generates hints that steer a frozen black-box LLM, no access to its weights requiredJuly 1, 20265 minJuly 1, 20265 min
Prompt Engineering, Properly · Part 3The Anatomy of a Good PromptInstruction, context, input, and output indicator, plus the design habits that make them workJune 29, 20267 minJune 29, 20267 min
Prompt Engineering, Properly · Part 2The Settings That Change Your OutputTemperature, top_p, max tokens, stop sequences, and the penalties, and when to touch eachJune 29, 20266 minJune 29, 20266 min
Agentic AI@Claude in Your Slack: What Claude Tag Actually ChangesAnthropic put an agent in the channel where the work already happens. Here is why that matters more than the model behind it.June 25, 20267 minJune 25, 20267 min
AI InfrastructureMonitoring Model Deprecation in ProductionThe model you pinned will be switched off one day, probably with less notice than you would like. Here is how to find out from CI instead of from your users.June 24, 202616 minJune 24, 202616 min
AI InfrastructureObservability for LLM Apps: What to Log, What to Alert OnYour 500s and latency graphs will look fine while the product quietly gives wrong answers. LLM observability is about catching the failures that do not throw.June 24, 20267 minJune 24, 20267 min
Agentic AIGraph, State Machine, or Plain Loop: How to Structure an AgentMost agents start as a while loop and should stay that way. Here is how to know when you have actually outgrown it.June 24, 20268 minJune 24, 20268 min
AI InfrastructureCaching Agent Tool Calls (Not Just Prompts)Prompt caching saves you tokens. Caching tool calls saves you the slow, flaky, expensive thing the tool actually does.June 24, 20269 minJune 24, 20269 min
RAGBuilding a Multi-Tenant RAG System: Isolation, Per-Tenant Indexes, and the Leaks Nobody Plans ForThe day one customer retrieves another customer's documents, it is over. Here is how to make sure that never happens.June 24, 20269 minJune 24, 20269 min
AI InfrastructureFive Projects to Actually Master AI Engineering (for Experienced Engineers)Skip the toy chatbots. Build the five systems that teach the hard parts: retrieval, agents, evals, cost, and reliability.June 23, 20267 minJune 23, 20267 min
Building with DeepAgents · Part 5Human-in-the-Loop in DeepAgents: Approve Before It ActsWhy some tool calls need a person, and how to gate them with interruptOn, decisions, and a checkpointerJune 23, 202611 minJune 23, 202611 min
Building with DeepAgents · Part 3Memory in DeepAgents: How Agents Learn Across ConversationsFilesystem-backed memory, user versus agent scope, and background consolidation, with the TypeScript to wire each oneJune 23, 20269 minJune 23, 20269 min
LLM IntegrationPrompt Caching for LLM Apps: What It Is and When It Pays OffHow providers cache your prompt prefix, the real discounts, and the prompt structure that decides whether you save anythingJune 22, 20266 minJune 22, 20266 min
Agentic AILangGraph State, Checkpointing, and Persistence ExplainedHow a graph remembers: state channels, checkpointers, threads, and the time travel you get for freeJune 22, 20267 minJune 22, 20267 min
LLM IntegrationCutting LLM Cost and Latency Without Wrecking QualityMeasure first, then reach for caching, routing, smaller models, and the structural fixes that actually move the numbersJune 22, 20267 minJune 22, 20267 min
Agentic AIGuardrails and Safety for Agents in ProductionDefense in depth for agents: input rails, output checks, tool limits, and the injection problem that will not fully go awayJune 22, 20267 minJune 22, 20267 min
LLM IntegrationStreaming LLM Responses End to End: Backend to UIWhy SSE won, how the token stream flows from the provider to the browser, and where it breaksJune 22, 20269 minJune 22, 20269 min
AI InfrastructureHow I Learn to Build Production AI Systems by Dissecting Open SourceThere are no good production-grade courses, so I turn the codebases real teams run into my curriculumJune 22, 20267 minJune 22, 20267 min
Agentic AI in Practice · Part 7Agent Memory: Short-Term vs Long-Term, and How to Wire It UpThreads are short-term memory. Stores are long-term memory. Most agent memory bugs come from confusing the two.June 20, 20267 minJune 20, 20267 min
LLM IntegrationStructured Outputs and Function Calling, In DepthHow to make an LLM return data that always fits your schema, and how function calling is the same idea wearing a different hatJune 20, 20267 minJune 20, 20267 min
Production RAG · Part 4Choosing a Vector Database in 2026: pgvector vs Pinecone vs Qdrant vs WeaviateMost teams should start with pgvector and only move when they have a real reason. Here is how to know when that day comes.June 20, 20266 minJune 20, 20266 min
Production RAG · Part 2Reranking in RAG: Cross-Encoders and When They Are Worth the LatencyA reranker can rescue a mediocre retriever or waste 200ms on an already-correct answer. Knowing which is the whole skill.June 20, 20265 minJune 20, 20265 min
Production RAG · Part 1Chunking Strategies for RAG: Fixed, Recursive, Semantic, and How to ChooseWhy recursive splitting is the right default, and the few cases where you should reach for something fancierJune 20, 20266 minJune 20, 20266 min