RAG or chatbot: what to hire an AI engineer for
A chatbot is the interface; RAG is the retrieval behind it. How a founder decides what to build and which expert to hire, in five steps.
A chatbot and RAG are not two rival products. A chatbot is the window people type into. RAG (retrieval-augmented generation) is a technique behind that window: before the model answers, the system looks up the right material from your own documents. If your bot must answer from your policies, prices or product details, you need RAG, and you should hire for it.
What each term means
AWS defines RAG as optimizing a language model's output so it draws on an authoritative knowledge base outside its training data before it responds (AWS, What is RAG). Without it, a model answers only from what it learned in training. With it, a retrieval step first pulls relevant material from a data source, and the question and that material go to the model together.
AWS's guidance adds that, from the user's side, RAG looks like using any language model, except that the system has access to the organisation's content (AWS Prescriptive Guidance). So the chat screen can be identical. What differs is what sits behind it.
When a plain chatbot is enough
- General questions that do not depend on your private information.
- A scripted flow: collect a name, a number and a preferred time, then pass it to a person.
- A first test of whether customers will use chat at all.
When you need RAG
- Answers must match your own documents: price lists, policies, manuals, course details.
- Those documents change, and the bot must not repeat last quarter's version.
- A wrong answer costs you a customer, so you want the bot to point to where an answer came from.
A simple example
Picture a coaching institute with a fee structure, batch timings and a refund policy, all in different files. A plain chatbot can greet parents and take a phone number. It cannot reliably say when the next batch starts, because that fact is not in the model. With RAG, the system first finds the batch-timing page, hands it to the model with the parent's question, and the answer comes from the institute's own page. When the timings change, you update the page, not the bot. That is the practical difference: the chat window looks the same, but the facts come from your documents, and you stay in control of them.
What a RAG system needs
| Part | What it is | What to ask a candidate |
|---|---|---|
| Clean documents | The material the bot reads | Who keeps it current, and how? |
| Retrieval | Finding the right passages for a question | How do you check it finds the right ones? |
| Guardrails | What the bot does when it does not know | What does it say when nothing matches? |
| Upkeep | Adding and updating documents | What is the weekly routine? |
AWS notes that loading documents and creating embeddings is a one-time upfront step, but that building and running a production RAG system takes significant work. Budget for the upkeep, not only the first build.
Five steps before you hire
- Write the ten questions your customers ask most, and where the true answer lives today.
- Tidy that material. A bot cannot be better than the pages it reads.
- Decide what happens when the bot is unsure. A handover to a person is a good default.
- Set a 30-day outcome, such as "answers these ten questions correctly from our documents". Our guide on testing a remote expert before committing shows how to judge a first month.
- Pick the role. An AI engineer builds and connects the system; an automation specialist wires tools you already pay for. Our guide to hiring a dedicated AI engineer in India covers the first.
How AnantaSutra fits
If you need someone to build and maintain this, a dedicated expert works inside your team, in your Slack and standups, on your priorities. You see 2-3 hand-picked, vetted experts and choose whom to interview, and we aim to have the expert live in about 7 days.
The price is one fixed monthly price, from ₹20,000 to ₹60,000 a month, depending on the expert's skills, experience and the work. No extra fees, no lock-in. You can scale up or pause anytime. See the dedicated experts service, and for the cost side of a developer hire, read the cost of a dedicated developer in India.
What to check in a candidate's work
- A bot they built that you can try, with a question it should refuse.
- How they explain a wrong answer: where retrieval failed, not "the AI got it wrong".
- How they document the setup, so your team can keep it running.
FAQ
Is RAG the same as a chatbot?
No. A chatbot is the interface people type into. RAG is a technique a chatbot can use to look up your own documents before the model answers.
Do I need RAG for a simple FAQ bot?
Not always. If the questions are few and fixed, a scripted flow can do. RAG matters when answers must come from documents that are long or change often.
What does a RAG project need besides an engineer?
Clean, current documents and someone who owns them. The system can only retrieve what you give it.
How fast can an expert start with AnantaSutra?
We aim to have an expert live in about 7 days, after you interview 2-3 vetted experts and choose.
What does it cost?
One fixed monthly price per expert, from ₹20,000 to ₹60,000 a month, depending on the expert's skills, experience and the work. No extra fees, no lock-in.
Ready to talk it through? Book a free consultation.
