LLMOps: Production-Grade LLM Operations
LLMOps is the engineering discipline that covers the development, deployment, monitoring, evaluation and cost management of LLM-powered applications — extending classic MLOps with prompt versioning, eval-driven CI and observability tailored for non-deterministic systems.
- LLMOps: Production-Grade LLM Operations
- LLMOps is the engineering discipline that covers the development, deployment, monitoring, evaluation and cost management of LLM-powered applications — extending classic MLOps with prompt versioning, eval-driven CI and observability tailored for non-deterministic systems.
What you will learn in this pillar
- 01Prompt versioning and eval-driven CI
- 02Observability with Langfuse / Helicone / Arize
- 03Cost optimization: caching, routing, batch API
- 04Hallucination and drift monitoring
- 05Fine-tuning: LoRA, QLoRA, instruct tuning
- 06Canary deploys, A/B testing and shadow traffic
In-depth Explanation
Blog posts on this pillar
DPO, LoRA, and QLoRA: A Practical Fine-Tuning Guide for 2026
The 2026 fine-tuning stack: base → SFT → DPO. I explain preference optimization, LoRA/QLoRA, and when to fine-tune instead of using RAG, from the field.
DPO, LoRA, and QLoRA: A Practical Fine-Tuning Guide for 2026 →
From Zero to AI Engineer in 2026: 12 Months, 5 Production-Level Projects, $200K+ Job Offer
A concrete roadmap to land a global remote AI Engineer position from zero in 12 months: 5 production-level projects, GitHub portfolio + blog strategy, $200K+ offer. Karpathy, Raschka, 3Blue1Brown, Andrew Ng curriculum; HuggingFace + LangChain + Anthropic Academy free programs; Turkish alternatives; case study (14-month timeline); and interview strategy for top offers.
From Zero to AI Engineer in 2026: 12 Months, 5 Production-Level Projects, $200K+ Job Offer →
RAG or Fine-Tuning? Which Approach Is Better for Which Scenario?
One of the most important strategic questions in enterprise AI is whether a problem should be solved with RAG or with fine-tuning. Many teams treat these approaches as direct alternatives, but in reality they solve different classes of problems. RAG strengthens access to current and source-grounded knowledge, while fine-tuning shapes model behavior and task adaptation. This guide compares RAG and fine-tuning across technical, operational, cost, governance, and production-readiness dimensions, and explains when each approach is the right choice—and when a hybrid architecture makes more sense.
RAG or Fine-Tuning? Which Approach Is Better for Which Scenario? →
What Is LLM Observability? A Guide to Production Monitoring and Tracing
What is LLM observability? LLM observability is the practice of tracing every request of a language model application end to end, making prompts, responses, latency, cost, and quality visible. This guide: a clear definition, why it matters, how tracing works, Langfuse and OpenTelemetry, production monitoring metrics, evaluation, KVKK, and FAQs.
What Is LLM Observability? A Guide to Production Monitoring and Tracing →
The 2026 Adaptation Order: Prompt → RAG → Fine-tune → Distillation with LoRA/QLoRA
Fine-tuning shapes behavior; RAG supplies knowledge. The right 2026 order: prompt first, then RAG, then LoRA/QLoRA, distillation last. A field decision guide.
The 2026 Adaptation Order: Prompt → RAG → Fine-tune → Distillation with LoRA/QLoRA →
LLM Fine-Tuning: A Comprehensive 2026 Guide to LoRA, QLoRA, DPO, and Modern Alignment
The most current, detailed 2026 Turkish guide to adapting an LLM to your domain. Covers when fine-tuning is necessary, the math behind LoRA, 4-bit training with QLoRA, why DPO beats PPO, modern alternatives (ORPO/KTO/IPO), Turkish dataset sources, GPU/cloud cost modeling, production pipelines, 3 anonymized Turkish enterprise case studies, and KVKK-compliant training. For developers, MLOps engineers, and AI architects.
LLM Fine-Tuning: A Comprehensive 2026 Guide to LoRA, QLoRA, DPO, and Modern Alignment →
Learning content
Observability: Logging, Tracing, LangSmith / Langfuse
Production LLM gözlemlenebilirliği: structured logs, distributed tracing, anomaly detection. LangSmith, Langfuse, Helicone karşılaştırması.
Observability: Logging, Tracing, LangSmith / Langfuse →
Full Telemetry Tools Comparison: Langfuse vs Helicone vs LangSmith vs Phoenix vs OTel
We compare the 5 main LLM observability tools side-by-side: feature sets, pricing, self-host options, KVKK compliance, integration ease. Decision matrix for 'which one should I use in my case'.
Full Telemetry Tools Comparison: Langfuse vs Helicone vs LangSmith vs Phoenix vs OTel →
Related training
Frequently Asked Questions
What changes when moving from MLOps to LLMOps?▾
Three main shifts: (1) you manage prompt + retrieval + tool stacks rather than training models from scratch; (2) deterministic metrics give way to eval sets and LLM-as-judge scoring; (3) cost strategy moves from GPU planning to token economics and caching.
Which observability tool should I start with?▾
Self-hosted / open-source: Langfuse. Fast SaaS start: Helicone or LangSmith. Multi-model focus: Arize Phoenix. Key requirement: traces, prompt versions, eval scores and cost in a single pane.
When is fine-tuning actually needed?▾
Three legitimate cases: (1) brand/voice consistency, (2) latency or cost targets (fine-tuning a smaller open model to save inference), (3) domain-specific behavior unreachable via prompting. Otherwise, exhaust prompting + RAG first.
How can token cost be aggressively reduced?▾
Step ladder: (1) Anthropic prompt caching, (2) semantic cache (Redis + embeddings), (3) model tiering (Haiku/Mini-Sonnet → Opus escalation), (4) per-prompt budget caps, (5) batch APIs. Combined, these typically yield 50–70% savings.
How big should an eval set be?▾
Pragmatic start: 50 'golden' examples plus 200 sampled from real production traffic — about 250 total. Each LLM-judge run lands around $1–$3. In CI, run a 30-sample smoke set per PR and the full set nightly.
How is canary deploy done with LLMs?▾
Two routes: (1) traffic split — send 5% of users to the new prompt/model; (2) shadow traffic — run the new version in parallel with the old and compare metrics. The shadow approach is preferred since it isolates user experience from risk.
Other pillar topics
Enterprise AI Consulting
Enterprise AI consulting is the end-to-end discipline that takes AI from business objectives to technical architecture, prioritizing use-cases and shaping a production-ready roadmap so AI scales sustainably inside the organization.
RAG (Retrieval-Augmented Generation) Architecture
RAG (Retrieval-Augmented Generation) is an architecture that grounds large-language-model answers in chunks retrieved from the organization's own documents or data sources, providing both freshness and citations.
Agentic AI and Autonomous Systems
Agentic AI is the architecture in which a large language model — instead of producing a single answer — autonomously completes multi-step tasks by combining planning, tool use, memory and feedback loops.
AI Governance and EU AI Act Compliance
AI Governance is the corporate framework that ensures AI systems — from design to use — meet ethical, safety, transparency, explainability and legal-compliance requirements (EU AI Act, GDPR/KVKK, ISO 42001).
Corporate AI Training
Corporate AI training is a structured program — calibrated to different role levels from executives to engineers — that builds AI capability through hands-on, scenario-grounded learning with measurable outcomes.
Industry AI Use Cases
AI use cases are a pragmatic decision guide — across banking, healthcare, retail, public sector and beyond — capturing the concrete business value, success metrics and reference architectures that make AI worth building.
Prompt and Context Engineering
Prompt engineering is the applied discipline of designing instructions, examples, context and output controls so that an LLM produces consistent, accurate and cost-efficient outputs.
Digital Transformation
Digital transformation is not layering technology onto existing processes; it is redesigning the business model, operating model and decision mechanisms around what digital capability makes possible — a management discipline rather than a technology project.
AI Transformation
AI transformation is the process of embedding AI into the organization's decision mechanisms on top of the process, data and operating-model foundation that digital transformation established — not adding AI as a tool, but restructuring the flow of work around AI capability.
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