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Guide 03 — LLM · RAG · Fine-tuning · Tools · Agents
Five building blocks. Different jobs. Better AI systems.
What each GenAI building block actually does, at runtime or at training time, and when to reach for which.
A4 sheet — scroll sideways or pinch to zoom
Visual Guide 03 · GenAI / Agentic / Builder
LLM · RAG · Fine-tuning · Tools · Agents
Five building blocks. Different jobs. Better AI systems.
Healthcare AI Learning
| Term | LLMAI model | RAGArchitecture pattern | Fine-tuningTraining method | ToolsExternal capabilities | AgentsSystem pattern |
|---|---|---|---|---|---|
| What is it? | Pretrained model that processes and generates language or other content. | Retrieve external information and give it to the model as context. | Continue training or adapt a pretrained model on task or domain examples to change its behaviour. | Defined functions or services an AI application can invoke to get data or perform actions. | An AI system that can choose and sequence steps and tool use toward a goal, within boundaries. |
| Main purpose | General reasoning, understanding, transformation and generation. | Bring relevant, current or private information into a response without retraining the model. | Improve format, style, task behaviour or domain performance when examples teach the desired pattern. | Read, search, calculate, write, or call external systems. | Multi-step tasks that benefit from iterative planning, action and feedback. |
| Runtime / training | Uses prompt and context at runtime. Internal knowledge comes from training and does not automatically include current or private local data. | Runtime mechanism. Does not change base model weights. | Training-time adaptation: updates model parameters or adapters. Not retrieval. | Runtime. Can be exposed through APIs or MCP. Fixed workflows can use tools without being agents. | Terminology varies. In this learning platform an agent is a system that can direct at least some of its own process and tool use in a loop, rather than following only a fixed script — with stopping rules, permissions and oversight. |
| Healthcare example | Draft a clinical summary from supplied patient context. | Retrieve a current guideline plus recent HbA1c and condition context before drafting a response. | Train toward a consistent structured documentation format using approved labelled examples. | Call a FHIR endpoint, calculate a score, create a draft workflow task. | Gather approved context, use approved tools, draft a result, check conditions and escalate when permissions or quality rules require it. |
| What it is NOT | Not a source of truth, and not a hospital database. | Not a model, not a database, and not a guarantee of correctness. | Usually not the primary mechanism for rapidly changing or traceability-sensitive facts. | Not the same thing as an agent. | Not automatically better than a simpler workflow. |
Runtime flow
Task / user goal
RAG / context
LLM
Tools
Agent / workflow (when appropriate)
Fine-tuning (training time)Fine-tuning is a training-time arrow into the LLM, not a step in the runtime flow.
Common misconceptions
- “RAG and fine-tuning solve the same problem.” No — runtime context versus training-time adaptation.
- “If a model uses tools it is an agent.” Not necessarily. Fixed workflows can use tools.
- “Agents are always better.” No. Use the simplest safe architecture that meets the task.
- “Fine-tuning is how you teach the latest facts.” Generally not, for frequently changing or traceability-sensitive knowledge; retrieval is usually better suited.
- “RAG removes hallucinations.” No. Retrieval and generation can still fail, so evaluation stays necessary.
Related learning
Guides summarise the same primary sources cited in the related courses — official EU legal texts, standards bodies and peer-reviewed literature. Educational summaries only, not legal or clinical advice; regulatory dates were checked between 25 August and 10 September 2026.
Updated Sep 2026