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 →
RAG or Fine-tuning? The 2026 Decision Framework (LoRA, QLoRA, RFT, GRPO)
Fine-tuning teaches behavior, RAG brings knowledge. The 'Prompt → RAG → Fine-tune → Distill' decision framework with LoRA/QLoRA adapters, RFT, and small language models.
RAG or Fine-tuning? The 2026 Decision Framework (LoRA, QLoRA, RFT, GRPO) →
RAG or Fine-tuning? The 2026 Decision Framework: LoRA, QLoRA, and Distillation
A false dilemma: the right answer is Prompt → RAG → Fine-tune → Distill. Fine-tuning is for form, not facts. LoRA/QLoRA and the Turkish/KVKK dimension.
RAG or Fine-tuning? The 2026 Decision Framework: LoRA, QLoRA, and Distillation →
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 →
Comparing the AI Engineering Stack: Orchestration, Deployment, Observability, and Evaluation Layers
Production-grade AI systems require far more than choosing a model or framework. Real success depends on how well orchestration, deployment, observability, evaluation, security, and governance layers work together. This guide compares the core layers of the AI engineering stack, explains what each layer is responsible for, where teams make the wrong architectural decisions, and how organizations can build a more reliable and scalable AI operating model.
Comparing the AI Engineering Stack: Orchestration, Deployment, Observability, and Evaluation Layers →
Small Language Models and Fine-Tuning: The Path to Cost-Effective Customization in 2026 (LoRA, QLoRA, Distillation)
Small language models and fine-tuning: cost-effective customization with LoRA, QLoRA, and distillation. When an SLM beats a big API, and RAG vs FT.
Small Language Models and Fine-Tuning: The Path to Cost-Effective Customization in 2026 (LoRA, QLoRA, Distillation) →
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 →
Workshop Toolkit: A Quick Tour of the 11 Tools We'll Use Throughout the Course
Quick tour of the 11 key tools we'll use in the course: tiktoken, anthropic-tokenizer, Langfuse, Helicone, LiteLLM, vLLM, RouteLLM, LLMLingua, GPTCache, tldraw, Python uv. For each: what it does, when it kicks in, free or paid.
Workshop Toolkit: A Quick Tour of the 11 Tools We'll Use Throughout the Course →
LoRA + QLoRA: Parameter-Efficient Fine-Tuning Revolution — From Hu 2021 to Dettmers 2023
LoRA (Hu 2021): low-rank decomposition fine-tuning — base weights frozen, train only small adapter. %1 parameters, %95+ quality preservation. QLoRA (Dettmers 2023): 4-bit base + LoRA, fine-tune 70B model on consumer GPU. NF4 quantization, paged optimizer. Turkish practical: $5K cost production Turkish Llama-3 70B.
LoRA + QLoRA: Parameter-Efficient Fine-Tuning Revolution — From Hu 2021 to Dettmers 2023 →
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
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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
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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.
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