Guides
Prompt engineering training or LLM/RAG development training?
Short answer
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/RAG 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.
Key concepts
Prompt engineering: The deliberate structuring of instructions, context, examples, and output format to obtain consistent and accurate output from the model.
RAG (Retrieval-Augmented Generation): An architecture in which the model retrieves relevant parts of the organization's documents before generating a response and uses them as context.
Vector database: A database that stores text as numerical vectors (embeddings) representing their meaning and finds the semantically closest parts.
What is the difference between prompt engineering and LLM/RAG development training?
Prompt engineering training teaches how to use a ready-made tool more effectively, while LLM/RAG development training teaches how to build a new AI application specific to the organization.
Criteria | Prompt Engineering Training | LLM/RAG Development Training |
|---|---|---|
Target audience | Business units, analysts, knowledge workers | Software developers, data and AI engineers |
Prerequisite | No coding knowledge required | Python and API usage |
What is learned | Task description, providing context, example-based guidance, output verification | Model APIs, document chunking, embedding, vector databases, access permissions, evaluation |
Outcome | Faster and more consistent daily work | A working prototype or production system |
Success metric | Tool usage, task duration | Answer accuracy, response time, cost |
Academy Club's AI catalog covers both sides: on one hand Prompt Engineering & OpenAI and Generative AI Tools and Effective Usage, and on the other hand LLM Orchestration & LangChain and Advanced RAG & Enterprise LLM Applications.
When is prompt engineering training sufficient?
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.
Text-heavy work: Teams preparing reports, emails, presentations, and summaries.
An enterprise assistant already exists: If the organization has already provided an approved assistant, employees need to know how to get the right output from it.
When is LLM/RAG development training necessary?
LLM/RAG development training is necessary when AI needs to be connected to the organization's own knowledge sources or embedded into a business process.
Internal knowledge assistant: A system that answers questions based on policy, procedure, or product documents.
Process automation: Applications that classify requests or extract fields from documents.
Agent systems: Applications that plan multiple steps and connect to enterprise systems; this level requires advanced content such as Agentic AI & MCP.
Should technical teams take both trainings together?
Yes; prompt design at the developer level is part of LLM/RAG 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/CD processes (news).
How do you decide which training to choose?
The decision is made based on the expected outcome and the team's technical infrastructure.
Define the outcome. Is the goal for people to work more efficiently, or to build a new application?
Determine the data requirements. Is it necessary to connect to the organization's documents or systems?
Check prerequisites. Are there people on the team with Python and API experience?
Start with a single scenario. Build the development training around a concrete use case.
Observations from the field: Where do organizations struggle with this choice?
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.
The request for "an assistant that works with our own data" cannot be solved by prompt writing. 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.
Participation in development training without prerequisites reduces efficiency. Participants without Python and API experience spend the first day on setup and syntax. A brief prerequisite check helps set up the group correctly.
[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.]
Closing
Prompt engineering training enables more efficient work with ready-made tools, while LLM/RAG 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 training catalog and submit a request for the trainings you are interested in.
Frequently Asked Questions
Isn't it better to retrain the model with our own data (fine-tuning) instead of RAG?
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.
Will prompt engineering become unnecessary in the future?
Models are increasingly good at understanding simple instructions. However, clearly describing the task and verifying the output remain lasting skills.
What programming language is required for LLM/RAG training?
Most libraries in the ecosystem are Python-based. The language the team uses should be clarified with the provider before the training.
Can organizational data be used in the training?
It can be used with a confidentiality agreement and anonymization. A sample document set resembling real data is often sufficient.
Does it make sense to take the two trainings in sequence?
For technical teams, yes: first a short prompt design module, then development training. For business units, development training is generally not necessary.