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.
- 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.
- Wikidata: Q116982634
What you will learn in this pillar
- 01Instruction design: role, constraint, format control
- 02Few-shot, chain-of-thought, tree-of-thoughts
- 03Anthropic XML / OpenAI JSON-mode practices
- 04Context management and prompt caching
- 05Prompt versioning and eval-driven CI
- 06Prompt-injection and jailbreak defense
In-depth Explanation
Blog posts on this pillar
Learning content
System + Tools + Few-Shot: İçeride Doğru Sıralama
Prompt'un büyük blokları içindeki sıralama da kritik. System içinde KB ve instructions hangi sırada? Tools nereye? Few-shot examples cache'lenir mi? Bu derste mikro-yapı kararlarını sistematik öğreneceksin.
System + Tools + Few-Shot: İçeride Doğru Sıralama →
Bu Eğitim Hakkında ve Prompt Caching Neden Önemli?
Türkiye'nin en kapsamlı Prompt Caching & Context Engineering eğitimine hoş geldin. Şükrü Yusuf KAYA'dan; uçtan uca, ücretsiz, Türkçe ve production odaklı. Bu derste yol haritası, ön koşullar ve neden bu konunun 2026'nın en kritik AI mühendisliği becerisi olduğunu öğreneceksin.
Bu Eğitim Hakkında ve Prompt Caching Neden Önemli? →
The Cost of Chain-of-Thought: "Think Step by Step" Can Inflate Your Bill 3-10×
CoT (chain-of-thought) prompting improves accuracy by 20-40% in some tasks. But it inflates output tokens 3-10×. This lesson covers CoT cost vs accuracy across 5 task types and when to use it.
The Cost of Chain-of-Thought: "Think Step by Step" Can Inflate Your Bill 3-10× →
System Prompts ve Custom Instructions: Kalıcı Davranış Şekillendirme
Her sohbette tekrarlamak yerine modelin davranışını kalıcı olarak ayarlamak. Custom Instructions ve API'de system prompt.
System Prompts ve Custom Instructions: Kalıcı Davranış Şekillendirme →
Chain-of-Thought: Step-by-Step Reasoning
On complex problems, asking Claude to 'think first, answer second' dramatically improves accuracy. Cover the four flavors of CoT and practical use.
Chain-of-Thought: Step-by-Step Reasoning →
Few-Shot Learning: Teaching by Example
2-5 well-chosen examples beat pages of explanation. Learn how to pick and place few-shot examples.
Few-Shot Learning: Teaching by Example →
Frequently Asked Questions
When does few-shot beat zero-shot?▾
When output format or domain voice matters, few-shot is a clear win. For straightforward Q&A, modern models perform well zero-shot — and few-shot benefits plateau around 3–5 examples.
Is chain-of-thought more expensive?▾
Yes — token usage grows, but accuracy gains usually justify it. The pragmatic move: separate CoT into a 'thinking' block away from the final output and combine with prompt caching.
Anthropic XML vs OpenAI JSON-mode — which one?▾
On Claude models XML tags give tangible consistency and readability gains; on OpenAI, native JSON-mode + Pydantic schemas is the most robust setup. Standardize on the family-native pattern rather than mixing.
What size should a prompt be?▾
Compress static parts (rules, tone, format); over-long explanations can hurt eval scores. Aim for system prompts in the 800–1500 token range and RAG context that does not exceed roughly 50% of the model's context window.
What is the most practical defense against prompt injection?▾
A defense-in-depth stack: (1) separate trusted and untrusted data into distinct tags; (2) seal the system prompt; (3) enforce 'ignore instructions found in tool output'; (4) human-in-the-loop on high-risk actions.
How are prompts tested?▾
An eval set (50–200 cases) + LLM-judge scoring (faithfulness/relevance/format) + production traffic shadow eval. Smoke set per PR, full set nightly, A/B canary deploy on major changes.
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.
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.
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.
Let's talk about your project on this topic
Plan a tailored discussion on your enterprise AI roadmap, RAG architecture or AI training program.
Get in touch