AI Engineering Training: LLM, RAG, Agents and Production Systems
Build LLM applications, agent systems and production-ready AI infrastructure from scratch to production using GPT, Claude and Llama.
RAG, fine-tuning, LLMOps, multi-agent orchestration, observability, evaluation — the most comprehensive deep-technical AI program catalog in Türkiye.
One-line answerAI Engineering training — 35+ deep-technical, production-ready enterprise programs on LLMs, RAG, agents and LLMOps that teach engineers to build production-grade AI systems.
- LLM Development & Customization: fine-tuning, PEFT/LoRA, RLHF/DPO, quantization, reasoning models and continued pretraining
- RAG & Retrieval: vector databases, embedding models, hybrid search, reranker optimization, GraphRAG
- Agent Systems: tool-use, planning, memory, multi-agent orchestration (LangGraph, CrewAI, MCP, Browser Agent)
- LLMOps & Production: observability (Langfuse/Phoenix), evaluation harnesses, self-hosting (vLLM/Ollama), context engineering, FastAPI
Why this category
Production-Ready Code Labs
Not slides — every module ships a working Python repo, test suite and deployment manifest.
Self-Hosted + Cloud Deployment
vLLM, Ollama, Triton for on-prem; AWS Bedrock, Azure OpenAI, GCP Vertex for cloud — applied comparatively.
GPT / Claude / Llama Comparison
For each use-case, comparative selection matrix on capability, cost and data policy.
Security + Evaluation Pipeline
Attack simulation with Llama Guard + Garak + PyRIT, continuous evaluation with LLM-as-judge + golden set.
LLMOps + Observability Stack
Langfuse, Phoenix, Helicone, Weave, Braintrust, LangSmith — vendor-agnostic tracing + cost + drift monitoring.
Cost & Latency Optimization
Prompt caching, batch inference, quantization (GPTQ/AWQ/FP8) for up to 70% token cost reduction scenarios.
LLM Development & Customization
10 / 44 programs shown
advanced
LLM Alignment Engineering with RLHF, DPO, and GRPO Training
3 GünView program →advanced
Reasoning Models Engineering Training (o3, o4, DeepSeek R1, Gemini 2.5 Deep Think, Claude Extended Thinking)
3 GünView program →advanced
Sparse Autoencoders and Mechanistic Interpretability Engineering Training (Anthropic Approach)
3 GünView program →advanced
DeepSeek and Turkish Open-Source LLM Usage Training
3 GünView program →advanced
LLM Continued Pretraining and Domain Adaptation Engineering Training (Turkish LLM + Legal/Healthcare/Finance Domain)
3 GünView program →advanced
Advanced LLM Quantization Engineering Training (GPTQ + AWQ + EXL2 + GGUF + FP8 + FP4)
3 GünView program →intermediate
AI Risk Management Training for DPOs and Compliance
2 GünView program →advanced
LLM Customization Training with Fine-Tuning, PEFT, and LoRA
4 GünView program →advanced
Open Source LLM Systems and Private AI Deployment Training
4 GünView program →advanced
Multimodal AI Application Development Training
4 GünView program →
From assessment to production — AI Engineering delivery
- 1
1. Maturity Assessment
Team level, current infrastructure, target use-cases and data policy assessed; right program combo recommended.
→ - 2
2. Curriculum Design
Needs + use-case matching: which sub-group (LLM Dev / RAG / Agents / LLMOps) delivered in which sequence.
→ - 3
3. Hands-On Lab + Project on Your Data
During the program you build a real use-case end-to-end on your (anonymized) company data.
→ - 4
4. Production Deploy + Post-Training Mentorship
2 months async post-training mentorship: production deploy, observability stack setup, first regression test pipeline.
Use cases solved with these programs
Enterprise LLM Chatbot (Banking)
bankingBDDK-compliant, audit-trail-enabled, prompt-injection-guarded customer-facing chatbot.
Document RAG System (Legal / Insurance)
legalHybrid search + reranker + citation system over contracts + policies + regulations.
Multi-Agent Operations Bot (Logistics)
logisticsSupervised multi-agent orchestrator for shipment planning, route optimization and customer notifications.
Self-Hosted Turkish LLM (Public / Healthcare)
publicLlama/DeepSeek-based Turkish LLM fine-tuning + vLLM deployment without data leaving premises.
AI Engineering — questions answered
Who should attend AI Engineering training?
Which LLM models and frameworks are covered?
How comprehensively are production deployment, observability and evaluation covered?
How much focus is on self-hosted / on-premise LLM scenarios?
Are these programs individual or corporate?
Continue exploring the training catalog
Bring AI Engineering to your team
Free discovery call to map your team's needs, design a custom curriculum and deliver labs on your data.