genaillmragai

How Current Are Your LLM’s Answers, Really?

July 10, 20266 min read

Ever wondered how "current" your ChatGPT or Gemini answers actually are?

Every LLM has a cutoff date — a point after which it simply doesn't know anything. Ask it who won yesterday's match, or anything about your company's internal docs, and it'll either guess or straight-up hallucinate.

So how do u build a chatbot that actually knows your private data? That's where RAG comes in.

Let's break it down.

The knowledge cutoff problem

We need to train an LLM to update its knowledge base. So for every LLM there's a cutoff — the time at which it was last trained.

Like ChatGPT (GPT-5.5): trained till December 2025 Gemini (2.5 Pro): trained till January 2025 and so on.

So the LLM won't know anything after that time — like the score of a FIFA match, or which team qualified, etc.

Now say u want to ask about your company's internal docs. As we know, LLMs are trained on publicly available data, so they don't know anything about the private data your company has.

So what will u do if u want to build a chatbot that can answer queries related to internal docs? How will u do it?

You might think — just share everything with the LLM as a prompt. But u also know the LLM has a limit to how much it can remember, called the context window. If u exceed it, the model starts to forget previous context and will either hallucinate or give a wrong answer.

To fix this, we came up with the idea of RAG (Retrieval Augmented Generation).

What is RAG?

So basically, RAG is a way to give the LLM access to your private data — without retraining it, and without stuffing everything into the prompt.

The idea is simple: instead of relying only on what the LLM already knows, u first retrieve the relevant info from your own data, then pass only that relevant part to the LLM along with the question. So the LLM generates its answer based on fresh, relevant context — not just its old training data.

That's why it's called Retrieval + Augmented + Generation. U retrieve the right info, augment the prompt with it, and then the LLM generates the answer.

How a basic RAG pipeline works

At a high level, RAG runs in two phases. One happens once, the other happens live, every time someone asks something.

Phase 1 — Indexing (done once, offline)

U break your docs into chunks, convert each chunk into an embedding, and store all of them in a vector database.

Chunking — u can't just dump a 200-page doc into the LLM. So first, u break your docs into smaller chunks — like paragraphs or sections. This makes it easier to search through later.
Embeddings — now each chunk gets converted into something called an embedding — basically a list of numbers that represents the meaning of that text. Two chunks that mean similar things will have embeddings that are close to each other mathematically.
Vector database — all these embeddings get stored in a vector database (like Pinecone, Weaviate, or pgvector). Think of it as a special database built for searching by meaning, not just keywords.

Phase 2 — Query (done live, every time)

The user asks a question, that question gets converted into an embedding too, the system searches the vector database for the closest matching chunks, those chunks get added to the prompt along with the question, and the LLM generates its answer using that context.

Retrieval — the question's embedding gets compared against the database, and the system pulls out the chunks that are most relevant to what's being asked.
Augmentation — these top matching chunks get added to the prompt along with the user's original question. So now the LLM has actual relevant context to work with, instead of just guessing from its training data.
Generation — finally, the LLM reads the question + the retrieved context, and generates an answer based on that. So the answer is grounded in your actual data, not hallucinated.

Remember the context window issue we talked about? RAG solves that too — because instead of sending all your docs to the LLM, u only send the small relevant chunks. So u stay well within the context window limit, and the LLM doesn't lose track of what matters. And since the retrieved info is always fresh, it also solves the knowledge cutoff problem. The LLM doesn't need to "know" your internal docs — it just needs to read the right chunk at the right time.

Common scenarios where RAG works well

customer support bots grounded in help docs
internal company chatbots for HR policies, engineering wikis, etc.
Q&A over large PDFs or reports
search over legal or contract documents
product documentation assistants

Simple example — u ask a chatbot "what's our leave policy." Without RAG, the model has zero idea and just guesses. With RAG, it retrieves the actual HR doc paragraph and answers based on that.

Why RAG sometimes gives incorrect answers

Even though RAG grounds the LLM in real data, it's not magic. The final answer is only as good as two things — what actually gets retrieved, and how well the LLM uses it. If either one breaks, the answer breaks too.

Poor retrieval and missing context

If the vector search fails to find the relevant chunk — maybe because the question is phrased very differently from how the doc is written, or the embeddings just don't capture the right meaning — the LLM never sees the info it actually needed. And here's the tricky part: it doesn't know that it's missing something. It'll still answer, just wrong, or straight-up make something up.

Example — the doc says "refunds within 30 days," but if that exact chunk doesn't get retrieved, the LLM might just guess what the policy probably is.

Poor chunking and its impact on responses

If chunks are too big, u waste context window space and mix unrelated info together. If chunks are too small, u lose context — like a table row without its header, or a sentence without the paragraph that explains it. Chunking strategy has a bigger impact on answer quality than most people expect.

Context window limitations

RAG reduces the context problem but doesn't fully eliminate it. If u retrieve too many chunks, or the chunks themselves are too big, u can still overflow the context window. And more context isn't automatically better — stuffing in 20 chunks when only 2 were actually relevant can confuse the model or bury the important part.

Hallucinations even with RAG

RAG lowers hallucination risk, but it doesn't remove it completely. The LLM can still misread the retrieved context, blend it with stuff from its own training data, or add details that weren't actually in the chunk. RAG only helps if the model sticks to what was retrieved — and that's not guaranteed.

Keeping knowledge bases up to date

RAG solves the "LLM knows nothing after cutoff" problem for the model's own training, but it hands u a new job — keeping the vector database in sync with your real docs. If u update an internal doc but forget to re-index it, RAG will confidently retrieve the old, stale version and answer from that.

When RAG is not the right solution

if your data is small enough to just fit directly in the prompt, RAG adds unnecessary complexity
if the answer needs complex reasoning or computation across the whole dataset — like aggregating numbers — retrieval-based lookup won't help u
if data changes every second, like stock prices, a live API call is a better fit than embeddings
if u need guaranteed, 100% factual accuracy for high-stakes stuff like medical or legal answers, RAG is a strong aid but shouldn't be the only safety net

Wrapping up

RAG doesn't make an LLM smarter — it makes it better informed. It fixes the knowledge cutoff problem and the private data problem, and keeps u within context window limits. But it's not a silver bullet — bad retrieval, bad chunking, and stale indexes can all still lead to wrong answers. Think of it like giving the model the right notes before an exam — it still has to read them properly and write a good answer.