Articles & Guides
Deep dives on each layer of running language models in production - from first principles to the operational details.
The foundations, in order.
New to LLMOps? Read these in sequence - they set up everything else.
What is LLMOps?
LLMOps is the discipline of deploying, monitoring and improving large language model applications after the prototype works. A practical guide to what it covers, why it matters, and where to start.
Jun 5, 2026 · Read → LLMOpsLLMOps vs MLOps: What changes with large language models?
LLMOps builds on MLOps but adds prompts-as-code, non-determinism, LLM-judged evaluation, prompt-injection security and live token budgets. A practical guide to what actually changes - and what carries over.
Jun 4, 2026 · Read → LLMOpsThe LLMOps Stack: The 8 layers of production LLM systems
Prompt management, evaluation, observability, cost control, RAG operations, security, governance and deployment - a deep dive into the eight layers between an LLM prototype and production, with failure modes and a checklist for each.
Jun 3, 2026 · Read → ObservabilityLLM Observability: What to monitor in production
A production guide to LLM observability - the signals that matter, a runnable OpenTelemetry trace, the dashboard and alerts worth having, what to keep, and a complaint-to-root-cause walkthrough.
Jun 2, 2026 · Read →Newest guides first.
Everything else, most recently published at the top.
A short history of LLMOps: from hidden technical debt to agents
How LLMOps took shape, 2015-2026: the MLOps warning about technical debt, models turning into APIs, the 2023 production scramble, standards and regulation, and the agent era. Every milestone dated and linked to its primary source.
Sep 27, 2026 · Read → LLMOpsThe breakthroughs behind LLMOps: what each one changed in production
The papers and launches, 2017-2024, that made large language models capable, cheap to run, open and connected - and the operational work each one created. Every result taken from its primary source.
Sep 27, 2026 · Read → DeploymentYour model will be retired: an ops playbook for LLM migrations
Every model you build on has a deprecation calendar. A practical playbook for migrating LLM models without breaking production - inventory, pinning, eval gates, canary rollout and one-step rollback.
Jul 19, 2026 · Read → GovernanceOperating LLM agents in production: budgets, gates and escalation
An agent is an LLM with hands. An ops guide to running agents safely in production - least-privilege tools, approval gates for irreversible actions, step and cost budgets, per-step tracing and a real human escalation path.
Jul 19, 2026 · Read → EvaluationHow to build your first eval dataset
A practical, step-by-step guide to building an LLM eval dataset from real traffic - what a row looks like, how to score it, how many cases you need, and how to wire it into CI.
Jun 20, 2026 · Read → ObservabilityWhat to log in an LLM trace
A field-by-field guide to what belongs in a production LLM trace - request IDs, prompt versions, retrieval, tokens, latency, cost and outcome - plus what to redact.
Jun 19, 2026 · Read → EvaluationHow to calculate hallucination rate
A practical method for measuring LLM hallucination (faithfulness) rate in production - how to define it, sample it, judge it, and track it over time.
Jun 18, 2026 · Read → Prompt managementPrompt versioning with GitHub
A concrete workflow for versioning LLM prompts in GitHub - file layout, pull-request review, eval gating in CI, and one-step rollback - without buying a dedicated tool.
Jun 17, 2026 · Read → RAG operationsRAG freshness monitoring checklist
A focused checklist for keeping a RAG index fresh in production - detecting stale content, missing documents, embedding drift and re-index failures before users do.
Jun 16, 2026 · Read → GovernanceLLM incident response template
A ready-to-adapt incident response template for LLM applications - severity levels, the first 15 minutes, mitigation levers unique to LLMs, and a post-mortem structure.
Jun 15, 2026 · Read → ObservabilityHow to choose between Langfuse, LangSmith, Braintrust and Helicone
A decision framework for picking an LLM observability and evals platform - how Langfuse, LangSmith, Braintrust and Helicone differ, and which fits your team.
Jun 14, 2026 · Read → GovernanceWhat your CTO should ask before approving an LLM launch
The questions a technical leader should ask before signing off on shipping an LLM feature to production - covering evals, observability, cost, security, rollback and governance.
Jun 13, 2026 · Read → GovernanceLLM Governance: Audit trails, approvals and explainability
A practical guide to governing LLM applications - audit trails, approval gates, risk tiers, an evidence pack, a retention schedule, and what the EU AI Act asks for - so you can answer for the system, not just run it.
Jun 8, 2026 · Read → DeploymentLLM Deployment: CI/CD, staging and one-step rollback
How to ship LLM changes safely - CI with eval gates, shadow mode, a sticky canary with an automatic verdict, config-driven rollback and provider fallback that does not make things worse. With runnable code.
Jun 8, 2026 · Read → EvaluationLLM Evaluation: How to test prompts, RAG and agents
A production-grade guide to LLM evaluation - what breaks without it, what to measure, how to write an LLM-as-judge and an eval runner, and how to gate releases the way you gate code on tests.
Jun 1, 2026 · Read → Prompt managementPrompt Versioning: Why prompts should be treated like code
A one-line prompt edit can move behaviour as much as a model swap. A production guide to treating prompts as versioned artifacts - change-tested, attributable and rollbackable in one step.
May 31, 2026 · Read → RAG operationsRAGOps: How to monitor retrieval quality
Most "the model is wrong" bugs are retrieval bugs. A production guide to measuring retrieval quality, building a retrieval eval set, hybrid search with rank fusion, citation checks, chunking experiments and permission-aware retrieval.
May 30, 2026 · Read → Cost controlLLM Cost Control: Tokens, caching and model routing
Token spend compounds quietly until a finance review forces a panic. A production guide to unit economics, a worked cost model, token audits, caching, routing, batching and budget enforcement - before the bill, not after.
May 29, 2026 · Read → SecurityLLM Security: Prompt injection, data leakage and guardrails
An LLM with tools and data access is an attack surface. A practical guide to prompt injection, data leakage and excessive agency - with guardrail code, output sanitising, a blast-radius map and a red-team set to run in CI.
May 28, 2026 · Read → DeploymentLLMOps Checklist: From prototype to production
The narrative companion to the production-readiness checklist - the two questions that separate a demo from a system, walked across all eight layers of the LLMOps stack.
May 27, 2026 · Read →