Advanced Prompt Engineering Training (Anthropic + OpenAI Best Practices)

This training is designed for software developers, AI engineers, prompt engineers, content creators, and digital product leaders who want to build prompt engineering as a systematic model-behavior-shaping discipline, beyond the 'lucky word combination' perception. At the heart of the program is the following approach: advanced prompt engineering is not a 'trick collection.' Real engineering value comes from selecting the right model family, applying model-specific prompt-pattern standards (Anthropic XML tagging vs OpenAI's System / Developer / User hierarchy), correctly applying the 'less is more' principle for reasoning models (o1, o3, Claude Opus 4.7 Deep Think, DeepSeek R1), guaranteeing model behavior with schema-constrained outputs, building layered defenses against prompt-injection attacks, systematically measuring behavior with LLM-as-judge, and binding the entire system to a versionable, regression-protected prompt-engineering infrastructure.

The prompt engineering training ecosystem in Turkey has expanded rapidly over the past three years — institutions like BÜYEM, PwC Business School, Mindset Institute, Ari Bilgi, and Otusem offer comprehensive programs. However, the majority of existing programs remain at the level of 'basic prompt writing' and leave the following critical topics out of scope: the API differences between Anthropic's and OpenAI's official prompt-pattern standards, the specialized prompt approach for reasoning models, schema-constrained output engineering, prompt-injection and red-team defense, LLM-as-judge-based production evaluation frameworks, cost optimization with prompt caching, multimodal prompting (vision, audio, video), and meta-prompting. This training is designed to fill the advanced-level gap as Turkey's most comprehensive model-agnostic + production-grade reference program.

One of the program's strengths is the Anthropic + OpenAI comparative approach. Anthropic standardized XML-based prompt formatting, role definition, sequential-thinking structure, and Constitutional AI principles for Claude; OpenAI matured the System / Developer / User message hierarchy, structured outputs (response_format), function calling, and automatic prompt-caching mechanics for GPT-5 and the o-series. This training addresses both ecosystems in parallel, clearly lays out the API differences, and shows hands-on which approach is stronger in which scenario. Anthropic and OpenAI versions of prompts for the same task are written together, results compared, and architectural decision sets imparted.

Perhaps the program's most critical module is dedicated to reasoning models (o1, o3, Claude Opus 4.7 Deep Think, Gemini 2.5 Deep Think, DeepSeek R1). Reasoning models require a completely different prompting paradigm than standard LLMs. Chain-of-thought directives like 'Let's think step by step' cause performance drops in reasoning models; few-shot examples disrupt internal reasoning; overly detailed prompts trigger 'over-thinking' behavior. This training systematically teaches the 'less prompting, more thinking' principle of the reasoning-model paradigm, goal-first prompt structure, correct tuning of the reasoning_effort parameter (low/medium/high), and which tasks call for reasoning models vs standard models.

Advanced prompt architectures that improve reasoning quality in standard LLMs — Chain-of-Thought (CoT), Tree-of-Thought (ToT), ReAct, Reflexion, Self-Consistency, Plan-and-Solve — are addressed comparatively throughout the training. The strengths and weaknesses of each pattern, which problem class is the right choice for each, and how they combine with one another are shown through hands-on exercises. The few-shot learning module draws the boundary between static few-shot and dynamic few-shot; it covers building RAG-FS architectures via embedding-based example retrieval, example-pool management, and the curation discipline.

Schema-constrained output engineering is a critical component for production. Guaranteeing model output in JSON Schema, Pydantic, or XML formats ensures downstream systems run reliably. This training addresses the differences between OpenAI response_format and Anthropic schema-constraint APIs in detail; runtime validation with Pydantic, output retry, and fallback strategies are covered hands-on. Function and tool-calling design; writing tool names, descriptions, and input_schemas; measuring trigger accuracy; and parallel tool calls with dependency coordination are taught comprehensively.

The multimodal prompting module addresses how to effectively use the vision, audio, video, and document AI capabilities of GPT-5, Claude Opus 4.7, and Gemini 2.5 Pro at the prompt level. Real enterprise use cases like chart and screenshot analysis, OCR and handwriting recognition, multi-image comparison, meeting transcripts and sentiment analysis, video understanding, and PDF table extraction are addressed hands-on. Turkish-language content on multimodal prompting is extremely limited in Turkey, and this module is designed to fill that gap.

The prompt-injection, jailbreak, and red-team-defense module imparts a security-engineering discipline for production prompts. Direct injection (instruction override via user-controlled input), indirect injection (poisoning via tool / RAG documents), jailbreak patterns (DAN, roleplay attack, instruction override), and the OWASP LLM Top 10 attack taxonomy are covered in detail. As defense layers, input sanitization, NeMo Guardrails / LLM Guard / Anthropic safe-completions, output filtering, and post-process control with LLM-as-judge are set up hands-on. Red-team test-set preparation is addressed within the scope of open-source red-team tool ecosystems like Promptbench and garak.

Another module representing the program's production-quality discipline is dedicated to evaluation frameworks. Changing production prompts without measuring is high-risk for regression. This training teaches end to end: task-based gold-answer and rubric design; comparison of Promptfoo / OpenAI Evals / Anthropic Workbench; automated scoring with LLM-as-judge; judge-prompt engineering and bias-control techniques; inter-rater agreement and human-eval calibration; and setting up regression-test pipelines via GitHub Actions / GitLab CI. With prompt versioning and A/B test pipelines, prompt engineering begins to be managed like a software discipline.

In the capstone project, each participant combines all techniques learned throughout the training to design an end-to-end prompt architecture for their own enterprise use case. The capstone includes: (1) an Anthropic- or OpenAI-based production-grade prompt system, (2) a schema-constrained output and function-calling layer, (3) a prompt-injection defense and guardrail stack, (4) an LLM-as-judge-based evaluation framework, (5) a cost-optimization strategy through prompt caching and model routing, (6) a meta-prompting-based prompt-improvement workflow. By the end of the training, participants will reach a level where they can approach the advanced prompt-engineering discipline with a systematic engineering perspective, professionally apply model-specific best practices in the Anthropic and OpenAI ecosystems, build the right prompt architecture for reasoning models, manage multimodal use cases, protect production prompts with security and evaluation layers, and design enterprise prompt-engineering infrastructure. The training consists of 3 days, 12 modules, and over 80 hands-on lessons.