Learn the layer underneath the buzzwords: how an LLM call works, how to give it the right context, how to connect it to tools, and how to measure whether the result is good. Agents, RAG and MCP are patterns built from those pieces. Filter every new term by whether it solves a problem you actually have.
What people tell me
Every week there is a new term: agents, RAG, MCP, fine-tuning, vector databases, function calling, multi-agent systems. I bookmark tutorials for each one and never finish any. I cannot tell which of these are lasting ideas and which will be forgotten in six months, and I feel like I am always one term behind.
A composite of the messages behind this question, with personal details left out.
Key takeaways
- Most AI buzzwords are combinations of four ideas: a model call, context, tools and evaluation.
- Learn those four properly and new terms become easy to place.
- Use a simple filter: does this solve a problem I have in a project right now?
- Build one small project that uses a model end to end before reading about advanced patterns.
- Ignore anything you cannot explain in two sentences to a friend yet. It is not your next step.
The terms are newer than the ideas
Almost every AI term you hear is built from a small number of pieces. Once you see the pieces, the vocabulary stops being intimidating.
| Term | What it actually is |
|---|---|
| LLM | A model you send text to and get text back from, through an API or locally. |
| Prompt | The instructions and information you send with the request. |
| RAG | Finding relevant documents first, then putting them in the prompt so the answer is grounded in your data. |
| Tool or function calling | Letting the model ask your code to do something, such as search a database, and using the result. |
| MCP | A standard way to expose tools and data to models, so you do not write custom glue for each app. |
| Agent | A loop where the model decides the next step, calls tools, looks at results and continues until done. |
| Fine-tuning | Training a model further on your examples to change its behaviour or style. |
| Evaluation | Checking, with real test cases, whether the output is good enough. |
Look at that list again. There are really four ideas: calling a model, giving it the right context, letting it use tools, and measuring quality. Everything else is an arrangement of those four.
A filter for every new buzzword
When a new term appears, ask three questions before opening a tutorial:
- What problem does it solve? If you cannot find a plain answer, wait.
- Do I have that problem right now? If your project does not need it, bookmark it and move on.
- Which of the four pieces is it built from? If you understand those pieces, you will pick up the new term quickly when you do need it.
This filter alone will save you weeks. Most of what you feel behind on is stuff you do not need yet.
What I would learn, in order
- One LLM API, properly. Send requests, handle errors and timeouts, control cost, stream responses, and read the documentation rather than a thread about it.
- Structured output. Get the model to return JSON your code can trust, and handle the times it does not.
- Retrieval. Chunk some documents, search them, and put the results into the prompt. Notice where it fails.
- Tool calling. Let the model call one or two of your own functions. Then connect a tool through MCP so you see why a standard helps.
- Evaluation. Write twenty test questions with expected answers and score your system. This is the step most people skip and the one employers care about most.
- Agents last. Once you understand the pieces, an agent is a loop around them, and you will be able to judge when one is worth the extra complexity.
Build one thing all the way through
Reading about RAG for a month teaches less than building a small assistant over your own university notes or a set of documentation pages. Keep it modest: a simple interface, retrieval over a few hundred pages, one tool, and a small evaluation set. You will meet every real problem, from bad chunking to confident wrong answers, and you will understand the vocabulary because you needed it.
What tends to last and what tends to fade
I cannot predict which product names will survive, but I can tell you which skills have held up across every wave I have seen: clean software engineering, knowing your data, measuring quality, handling failures, and explaining trade-offs. Specific frameworks come and go. If you invest mostly in the lasting layer and lightly in the fashionable one, you will not be caught out.
A four-week plan
| Week | Focus | Output |
|---|---|---|
| 1 | One LLM API and structured output | A script that turns messy text into clean JSON |
| 2 | Retrieval over your own documents | A question-answering tool over a small document set |
| 3 | Tool calling and MCP | The tool can look something up in a real database |
| 4 | Evaluation and write-up | A test set, a score, and a README explaining what you learned |
A self-check: do you actually understand a term?
For any buzzword you think you know, try answering these out loud:
- What problem does it solve, in one sentence, without using other buzzwords?
- What is the simplest thing someone could do instead, and why is this better?
- What goes wrong with it in practice?
For example, for RAG: "It lets a model answer from my own documents by searching them first and adding the best matches to the prompt. The simpler option is pasting the whole document in, which fails when documents are large or numerous. It goes wrong when the search returns the wrong chunks, so the model answers confidently from irrelevant text." If you can do that, you understand it well enough to use it. If you cannot, that is the one term worth studying next.
If you are still stuck
Read The AI Engineering Words You Keep Hearing, Explained Properly, RAG, Fine Tuning, or a Better Prompt: How to Choose and Six Ways to Structure Work for an AI Agent. If you want help picking a project that fits your level, join Sefism and, as a member, book a 1:1 session.
Was this answer helpful?
Read next
- The AI Engineering Words You Keep Hearing, Explained ProperlyThe concepts underneath AI engineering are mostly not difficult, but every explanation assumes eight other terms you have not met yet. Tokens, embeddings, RAG, fine tuning, tool calling, MCP, agents, evals and the rest, each with what it is genuinely good for and where it will let you down, plus the order worth learning them in.
- RAG, Fine Tuning, or a Better Prompt: How to ChooseEvery article recommends whatever that article happens to be about, and the real decision is more boring: match the problem to the cheapest technique that solves it, which is nearly always further down the list than people assume. A working order from prompting through retrieval, reranking, tools and agents, with the signal that tells you to move to the next step.
- Six Ways to Structure Work for an AI AgentThe word agent covers everything from a chatbot with one tool to a system that runs for twenty minutes deciding its own steps, and those do not fail the same way. Routing, parallelisation, orchestrator and workers, tool use, reflection and planning, what each is for, what goes wrong, and the durable execution work that separates a demo from something you can leave running.
Your situation is not quite this one?
Members get written answers to their own questions, a roadmap built for them and feedback on their projects. Early access is open to X and Instagram followers and university students. Prefer to talk? A free call works too.