#production
39 articles
Sandboxing AI Agents: Containers, gVisor, and MicroVMs
Sandboxing AI agents with containers, gVisor, Firecracker microVMs, and Wasm: honest boot and overhead numbers, and why egress filtering matters most.
Voice AI latency budget: hitting sub-500ms in production
A systematic breakdown of every millisecond in the voice AI pipeline and the specific techniques that compound to sub-500ms time-to-first-audio in production.
AI Compliance in Practice: EU AI Act, SOC 2, and HIPAA
Risk tiers, documentation duties, audit trails, and health-data rules for teams shipping LLMs under the EU AI Act, SOC 2, and HIPAA in 2026.
Voice Agent Frameworks: Vapi vs LiveKit vs Pipecat
Compare Vapi, LiveKit Agents, and Pipecat across latency, cost, telephony, and control — with the exact break-even math to pick the right abstraction.
Streaming LLM Responses: The Engineering Nobody Talks About
SSE vs WebSockets, partial-JSON parsing for streamed tool calls, backpressure, cancellation, and rendering streamed markdown without flicker — the complete engineering guide.
Deploying LLM Workloads: GPUs, Kubernetes, and Serverless
Where LLM serving engines actually run: GPU cloud choices, Kubernetes patterns, serverless GPU tradeoffs, cold starts, and autoscaling strategies that hold up under real traffic.
VAD and Turn Detection: The Hardest UX Problem in Voice AI
How voice activity detection and turn-taking work in real-time AI agents — energy vs semantic VAD, barge-in handling, and why getting this wrong is the top user complaint.
From Prototype to Production: Scaling an AI Product
The concrete engineering decisions — model routing, caching strategy, async batching, context engineering, and cost governance — that turn a demo into a system handling real load.
Red-Teaming LLMs: From Manual Probing to Automated Adversarial Testing
How to run a structured red-team engagement against your LLM system — scoping, tooling (Garak, PyRIT, PromptFoo), automation, and what the results actually mean.
Build vs Buy and Small vs Large: The Framework for Model and Infrastructure Decisions
Self-hosting beats APIs only above ~$4,200/month. Small models win on latency and cost. Learn the decision framework that prevents both premature optimization and missed savings.
Tracing and observability tooling: LangSmith, Langfuse, Braintrust, and Arize compared
A production-honest comparison of LangSmith, Langfuse, Braintrust, and Arize Phoenix for LLM tracing, evaluation, and quality monitoring in 2026.
Structured Data Extraction with LLMs
How to turn unstructured documents into typed, validated records using Pydantic AI and instructor — the entity extraction patterns that actually hold in production.
Metadata Filtering, Scaling, and Production Cost Control
How metadata filtering interacts with ANN indexes, multi-tenancy patterns, quantization tradeoffs, and why your vector DB bill runs 2.5–4x over forecast at scale.
The AI Engineer's Toolkit: Python, uv, and a Sane Dev Setup
Set up a Python AI engineering environment with uv, proper API key hygiene, and the minimal stack to start calling frontier models without the usual friction.
The data flywheel: closing the LLM feedback loop in production
How to run a data flywheel in production: capture implicit and explicit user signals, curate them into fine-tuning data, and compound model quality every cycle.
Prompt Anti-Patterns: The Failure Modes That Ship to Production Silently
Ten documented prompt defects — instruction-stacking, few-shot pollution, persona-stuffing — and why they survive code review and only surface under production load.
A/B Testing Prompts and Models in Production
How to run statistically valid experiments on LLM prompt variants and model swaps — shadow mode, canary rollout, LLM-as-judge scoring, and when to call the winner.
Fallbacks, Retries, and Circuit Breakers: Building Reliable LLM Pipelines
How to handle 429s, provider outages, and transient failures in production LLM systems: exponential backoff, fallback chains, and circuit breakers explained with real code.
Building Reliable Tool-Using Systems: Patterns From Production
Production patterns for reliable LLM tool use: loop budgeting, validation layers, idempotency, fallbacks, and observability that prevent runaway agents.
Vector Database Landscape 2026: Pinecone, Qdrant, Weaviate, Milvus, pgvector
A hardheaded comparison of Pinecone, Qdrant, Weaviate, Milvus, and pgvector: architecture, real costs, hybrid search maturity, and when each wins in production.
Open vs. closed models: a decision framework, not a religion
A practical framework for choosing between open-weight and closed frontier models — covering cost math, data governance, performance gaps, and operational burden as of mid-2026.
Golden Datasets: Building the Ground Truth Your Evals Depend On
A concrete recipe for constructing and maintaining LLM evaluation datasets — sourcing, sizing, versioning, and wiring them into CI so regressions are caught before they ship.
Multi-Agent Orchestration: Patterns That Survive Production
Six orchestration topologies for multi-agent systems — from orchestrator-worker to swarm and adaptive planning — with real failure rates and cost math.
The tool-use round trip: agentic loops, stop reasons, and parallel calls
How the model-to-application protocol works: stop reasons, parallel tool_use blocks, context rot, and loop budgeting that keeps production agents alive.
Document AI and OCR in 2026: When to Use VLMs vs Classic Engines
VLMs now beat classical OCR on noisy documents, but the right choice depends on layout complexity, volume, and cost. A decision framework with real benchmarks.
Prompt Versioning and CI/CD for LLM Changes
How to treat prompts as versioned, tested artifacts and build a deployment pipeline that gates every change on automated evaluation before it reaches users.
Input and Output Guardrails in Production: NeMo Guardrails, Llama Guard, and Beyond
How to layer NeMo Guardrails, Llama Guard 3, and lightweight classifiers to defend LLM apps — latency costs, bypass rates, and what still fails.
Model routing and cascades: sending the right query to the right model
How to build rule-based routers, intent classifiers, and confidence-based cascades that cut your LLM API bill 60–85% while preserving 95% of frontier quality.
Calling Vision APIs in Production: GPT-4o, Claude, and Gemini Compared
Calling vision APIs in production: image tokens and cost math, resolution tradeoffs, OCR accuracy, and how GPT-4o, Claude, and Gemini actually compare.
Prompt caching deep dive: prefix caching, semantic caching, and when each wins
How provider-side prefix caching and application-side semantic caching work, what they cost, where each breaks, and the decision framework for combining them in production.
Offline vs Online Evaluation: Building Your Two-Layer Safety Net
Pre-deployment regression suites catch what you break; production sampling catches what happens to you. Here is how to build both layers and wire them together.
System Prompts: Design Patterns That Actually Hold Up in Production
Four-section structure, cache-friendly ordering, modular composition, and how to write system prompts that survive model upgrades and adversarial users.
AI Engineer vs ML Engineer: what each role actually owns
AI Engineer is a distinct role from ML Engineer and AI Researcher. Learn the skill boundaries, daily work, and career tradeoffs between each in 2025-2026.
Reference architecture for a production LLM application
The seven-layer blueprint for production LLM apps: ingress, AI gateway, context assembly, model execution, output validation, observability, and feedback store.
Embeddings and Vector Databases That Scale
What embeddings really encode, how ANN indexes work, which vector database to pick at your scale, and the metadata-filtering pitfalls that quietly destroy recall in production.
Eval First: Building the Safety Net Before You Need It
Why LLM eval is hard, how 50 real examples beat 5,000 synthetic ones, and how to wire offline CI gates and production sampling into a two-layer safety net.
Cost Control and Reliability Engineering
Cut your LLM API bill ~4x and eliminate 429 cascades with prompt caching, model routing, semantic caching, and circuit breakers. Worked math from $60k to $14k/month.
Guardrails and Security: Production Is an Attack Surface
Prompt injection, jailbreaks, OWASP LLM Top 10, and the layered controls that actually reduce blast radius in production AI systems.
LLMOps: Shipping and Operating AI Products
The operational layer that separates lasting AI products from perpetual pilots: gateways, eval pipelines, prompt CI/CD, observability, and the data flywheel.