[{"data":1,"prerenderedAt":44},["ShallowReactive",2],{"guide:prompt-engineering-egitimi-mi-llmrag-gelistirme-egitimi-mi:en":3},{"id":4,"title":5,"slug":6,"excerpt":7,"cover_image_url":8,"published_at":9,"reviewed_at":10,"body":11,"seo_title":12,"meta_description":12,"faq":13,"related_trainings":29,"updated_at":43},7,"Prompt engineering training or LLM\u002FRAG development training?","prompt-engineering-egitimi-mi-llmrag-gelistirme-egitimi-mi","Çalışanlarınız hazır yapay zeka araçlarını günlük işlerinde daha verimli kullanacaksa prompt engineering eğitimi; ekibiniz kurumun kendi verisiyle çalışan bir yapay zeka uygulaması geliştirecekse LLM\u002FRAG geliştirme eğitimi uygundur. İlki kod bilgisi gerektirmez ve geniş kitleye verilir; ikincisi yazılım ya da veri ekiplerine yöneliktir ve programlama bilgisi ister.","https:\u002F\u002Facademyclub.b-cdn.net\u002Fguides\u002Fd4fb3aa70395450a834e6723e25f058e.webp","2026-10-14T20:51:01.830869+03:00","2026-10-14","\u003Ch2>Short answer\u003C\u002Fh2>\u003Cp>If your employees will use ready-made AI tools more efficiently in their daily work, prompt engineering training is suitable; if your team will develop an AI application that works with the organization's own data, LLM\u002FRAG development training is appropriate. The former requires no coding knowledge and is given to a broad audience; the latter is aimed at software or data teams and requires programming knowledge.\u003C\u002Fp>\u003Ch2>Key concepts\u003C\u002Fh2>\u003Cp>\u003Cstrong>Prompt engineering:\u003C\u002Fstrong> The deliberate structuring of instructions, context, examples, and output format to obtain consistent and accurate output from the model.\u003C\u002Fp>\u003Cp>\u003Cstrong>RAG (Retrieval-Augmented Generation):\u003C\u002Fstrong> An architecture in which the model retrieves relevant parts of the organization's documents before generating a response and uses them as context.\u003C\u002Fp>\u003Cp>\u003Cstrong>Vector database:\u003C\u002Fstrong> A database that stores text as numerical vectors (embeddings) representing their meaning and finds the semantically closest parts.\u003C\u002Fp>\u003Ch2>What is the difference between prompt engineering and LLM\u002FRAG development training?\u003C\u002Fh2>\u003Cp>Prompt engineering training teaches how to use a ready-made tool more effectively, while LLM\u002FRAG development training teaches how to build a new AI application specific to the organization.\u003C\u002Fp>\u003Ctable>\u003Ctbody>\u003Ctr>\u003Cth colspan=\"1\" rowspan=\"1\">\u003Cp>Criteria\u003C\u002Fp>\u003C\u002Fth>\u003Cth colspan=\"1\" rowspan=\"1\">\u003Cp>Prompt Engineering Training\u003C\u002Fp>\u003C\u002Fth>\u003Cth colspan=\"1\" rowspan=\"1\">\u003Cp>LLM\u002FRAG Development Training\u003C\u002Fp>\u003C\u002Fth>\u003C\u002Ftr>\u003Ctr>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Target audience\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Business units, analysts, knowledge workers\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Software developers, data and AI engineers\u003C\u002Fp>\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Prerequisite\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>No coding knowledge required\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Python and API usage\u003C\u002Fp>\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>What is learned\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Task description, providing context, example-based guidance, output verification\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Model APIs, document chunking, embedding, vector databases, access permissions, evaluation\u003C\u002Fp>\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Outcome\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Faster and more consistent daily work\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>A working prototype or production system\u003C\u002Fp>\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Success metric\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Tool usage, task duration\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Answer accuracy, response time, cost\u003C\u002Fp>\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Cp>Academy Club's AI catalog covers both sides: on one hand \u003Ca target=\"_blank\" href=\"https:\u002F\u002Facademyclub.co\u002Fegitimler\u002Fprompt-engineering-openai-egitimi\" rel=\"noopener noreferrer\">Prompt Engineering & OpenAI\u003C\u002Fa> and \u003Ca target=\"_blank\" href=\"https:\u002F\u002Facademyclub.co\u002Fegitimler\u002Furetken-yapay-zeka-araclari-ve-efektif-kullanimlari-egitimi\" rel=\"noopener noreferrer\">Generative AI Tools and Effective Usage\u003C\u002Fa>, and on the other hand \u003Ca target=\"_blank\" href=\"https:\u002F\u002Facademyclub.co\u002Fegitimler\u002Fllm-orchestration-langchain-egitimi\" rel=\"noopener noreferrer\">LLM Orchestration & LangChain\u003C\u002Fa> and \u003Ca target=\"_blank\" href=\"https:\u002F\u002Facademyclub.co\u002Fegitimler\u002Fadvanced-rag-kurumsal-llm-uygulamalari\" rel=\"noopener noreferrer\">Advanced RAG & Enterprise LLM Applications\u003C\u002Fa>.\u003C\u002Fp>\u003Ch2>When is prompt engineering training sufficient?\u003C\u002Fh2>\u003Cp>If the need is simply to use ready-made tools more efficiently and there is no requirement to connect the organization's data to a system, prompt engineering training is sufficient.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cp>\u003Cstrong>Text-heavy work:\u003C\u002Fstrong> Teams preparing reports, emails, presentations, and summaries.\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cstrong>An enterprise assistant already exists:\u003C\u002Fstrong> If the organization has already provided an approved assistant, employees need to know how to get the right output from it.\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>When is LLM\u002FRAG development training necessary?\u003C\u002Fh2>\u003Cp>LLM\u002FRAG development training is necessary when AI needs to be connected to the organization's own knowledge sources or embedded into a business process.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cp>\u003Cstrong>Internal knowledge assistant:\u003C\u002Fstrong> A system that answers questions based on policy, procedure, or product documents.\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cstrong>Process automation:\u003C\u002Fstrong> Applications that classify requests or extract fields from documents.\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cstrong>Agent systems:\u003C\u002Fstrong> Applications that plan multiple steps and connect to enterprise systems; this level requires advanced content such as \u003Ca target=\"_blank\" href=\"https:\u002F\u002Facademyclub.co\u002Fegitimler\u002Fagentic-ai-mcp\" rel=\"noopener noreferrer\">Agentic AI & MCP\u003C\u002Fa>.\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Should technical teams take both trainings together?\u003C\u002Fh2>\u003Cp>Yes; prompt design at the developer level is part of LLM\u002FRAG development. Writing system instructions, requesting structured (e.g., JSON) output, and evaluating prompt changes with a test set are skills different from end-user prompt writing. For example, the training Academy Club provided for Softtech engineers covered running large language models with API and command-line tools and integrating them into CI\u002FCD processes (\u003Ca target=\"_blank\" href=\"https:\u002F\u002Facademyclub.co\u002Fhaberler\u002Fsofttech-cli-ile-kod-asistanlari-calistirma-egitimi\" rel=\"noopener noreferrer\">news\u003C\u002Fa>).\u003C\u002Fp>\u003Ch2>How do you decide which training to choose?\u003C\u002Fh2>\u003Cp>The decision is made based on the expected outcome and the team's technical infrastructure.\u003C\u002Fp>\u003Col>\u003Cli>\u003Cp>\u003Cstrong>Define the outcome.\u003C\u002Fstrong> Is the goal for people to work more efficiently, or to build a new application?\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cstrong>Determine the data requirements.\u003C\u002Fstrong> Is it necessary to connect to the organization's documents or systems?\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cstrong>Check prerequisites.\u003C\u002Fstrong> Are there people on the team with Python and API experience?\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cstrong>Start with a single scenario.\u003C\u002Fstrong> Build the development training around a concrete use case.\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Fol>\u003Ch2>Observations from the field: Where do organizations struggle with this choice?\u003C\u002Fh2>\u003Cp>Organizations most often struggle because they try to address a need requiring RAG with prompt engineering training alone. The observations below are based on recurring situations Academy Club has encountered in corporate technical training.\u003C\u002Fp>\u003Cp>\u003Cstrong>The request for \"an assistant that works with our own data\" cannot be solved by prompt writing.\u003C\u002Fstrong> Writing good prompts does not enable the model to find information it cannot access. This need is a development project and requires technical team training.\u003C\u002Fp>\u003Cp>\u003Cstrong>Participation in development training without prerequisites reduces efficiency.\u003C\u002Fstrong> Participants without Python and API experience spend the first day on setup and syntax. A brief prerequisite check helps set up the group correctly.\u003C\u002Fp>\u003Cp>[EDITOR: A 1-2 sentence opinion from the Academy Club trainer on the most common technical mistake in RAG projects should be added to this section.]\u003C\u002Fp>\u003Ch2>Closing\u003C\u002Fh2>\u003Cp>Prompt engineering training enables more efficient work with ready-made tools, while LLM\u002FRAG development training enables building new applications that work with organizational data. If you would like to plan a customized program for your team, you can review Academy Club's \u003Ca target=\"_blank\" href=\"https:\u002F\u002Facademyclub.co\u002Fegitimler\" rel=\"noopener noreferrer\">training catalog\u003C\u002Fa> and submit a request for the trainings you are interested in.\u003C\u002Fp>","",[14,17,20,23,26],{"answer":15,"question":16},"Not in most enterprise use cases. Fine-tuning is suitable for changing the model's tone or its behavior on a specific task; for access to up-to-date organizational knowledge, RAG is more flexible and less costly.","Isn't it better to retrain the model with our own data (fine-tuning) instead of RAG?",{"answer":18,"question":19},"Models are increasingly good at understanding simple instructions. However, clearly describing the task and verifying the output remain lasting skills.","Will prompt engineering become unnecessary in the future?",{"answer":21,"question":22},"Most libraries in the ecosystem are Python-based. The language the team uses should be clarified with the provider before the training.","What programming language is required for LLM\u002FRAG training?",{"answer":24,"question":25},"It can be used with a confidentiality agreement and anonymization. A sample document set resembling real data is often sufficient.","Can organizational data be used in the training?",{"answer":27,"question":28},"For technical teams, yes: first a short prompt design module, then development training. For business units, development training is generally not necessary.","Does it make sense to take the two trainings in sequence?",[30],{"id":31,"title":32,"slug":33,"summary":34,"cover_image_url":35,"category":36,"duration_hours":42,"level":12,"module_count":41},30,"Prompt Engineering & OpenAI Training","prompt-engineering-openai-egitimi","Artificial intelligence has evolved from being an abstract technology running in the background of companies to becoming central to everyday business processes. Large language models such as ChatGPT, GPT-4, and Claude are now used across dozens of domains—from customer service to marketing copy, software development to data analysis, and legal document review to HR processes. However, the key to truly leveraging these models lies in asking the right question in the right way: Prompt Engineering—the skill of designing and optimizing the instructions given to AI—has become one of today's most critical digital competencies.\n\nThis training teaches participants how OpenAI and similar large language models work, which prompt techniques produce more accurate and consistent results, and how to apply these tools to real business problems through hands-on examples. For corporate organizations, this training enables employees to transform AI tools from superficial use into a strategic productivity asset, saves time on repetitive tasks, standardizes the quality of content and reports, and turns AI literacy into an organizational competency.","https:\u002F\u002Facademyclub.b-cdn.net\u002Ftrainings\u002F981efe5ded574c238a15d8307557181e.webp",{"id":37,"name":38,"slug":39,"icon":40,"order":41},1,"Artificial Intelligence","yapay-zeka","pi-sparkles",0,null,"2026-10-15T16:12:34.948638+03:00",1793776599222]