Understanding Grounding Under the Hood

Prompting
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1

I am trying to get a clearer picture of how grounding works under the hood in large language models. I have two questions and I hope someone can help me to clarify.

1- From what I understand, RAG is the only mechanism used to ground LLM-generated responses. Is that correct? Are there other mechanisms that also serve this purpose?
2- In RAG, my impression is that external resources or document/chunks are retrieved and added to the prompt to augment it before being passed to the LLM. Is there typically any post-processing that verifies whether the generated output accurately reflects or cites those retrieved sources? Or is it all handled without any additional validation?

2

It can help to think of a large language model (LLM) as a very advanced probability calculator for predicting the next token in a sequence of text.

During pre-training, the model is exposed to massive amounts of human language. It learns patterns in how words, phrases, and ideas connect. These patterns are stored in the model’s weights — this is what gives it its “default” general knowledge. When you give it some text, it calculates the probability of each possible next token and chooses the most likely sequence.

Prompting is the process of adding specific context so that those probability calculations lead toward the kind of answer you want. For example, if you ask “What color is the sky?” the highest-probability answer from general training is “blue.” If you add more detail — “What color is the sky at 9:00 p.m. in Europe?” — the model’s probabilities shift toward sunset or nighttime colors.

Retrieval-Augmented Generation (RAG) extends this idea by bringing in external, relevant information at query time. A retrieval system finds chunks of text (often using vector similarity search) that are related to your question. These chunks are inserted into the prompt along with your question. Because that new context is part of the input, it changes the model’s probability distribution toward answers grounded in those retrieved facts.

Grounding through RAG can be thought of as two main workflows:

1. Retrieval and prompt building – finding the most relevant chunks, filtering out noisy or irrelevant results, and presenting the information in a way that makes it easy for the model to use.

2. Post-processing and verification – checking that the generated answer matches the retrieved information and doesn’t contradict it, and optionally doing further fact-checking with additional searches or systems.

It’s important to note that the LLM itself does not automatically validate whether its output exactly matches your sources. The responsibility for building the right prompt, choosing high-quality retrieved content, and performing verification steps falls on you or your application’s workflow.

While other approaches like fine-tuning or prompt engineering can change how the model writes or adapts to certain styles, they are usually not practical for grounding in factual accuracy across diverse or complex domains. RAG is often the most effective and flexible method for grounding, and combining it with external fact-checking can significantly improve reliability.

P.S. my ideas edited by AI for easier understanding of my “un-human” approach :joy: :joy: :joy:

3

Okay, that makes sense. From your explanation, I understand that the generated response may or may not be validated against the cited sources (depending on the system architecture). But is it possible for a response to be grounded without including citations or references? In other words, is the addition of citations and references a separate step that occurs during response generation, or does it happen during the validation process?

4

So the question is : what is grounding? Generally it is about augmenting the LLMs “knowledge” through addition of additional information; that may or may not be in the LLMs pre-training phase. In that context, then, by definition, grounding requires additional references.

It is important also to note that just because one adds additional information (and therefore “grounds the response”), it is not necessary that the answer is actually “correct”. This is why output guardrails (or validation) is neessary.

5

From you question, I suspect we are using different terms… So it may create a confusion.

By “validating” - I understand a workflow that classifies the response (the whole response text) as “valid” or “invalid” or somewhere in between (if your validation process is not binary). For me personally, validation of the response is the responsibility of the requestor (the process that requested LLM to generate some content by providing the input/prompt). Depending on the architecture it can be your app or the internal process of the API who requests the LLM generation on your behalf.

Ideally for me it will be my app/flow, so that I have no middle man between me and LLM and that is what gives me the maximum control (and maximum responsibilities).

What is your meaning of “validated against the cited sources”?

I don’t even imagine how a human would “ground” an answer without some previous knowledge which can be traced to its source/citation… That is not what I would call “grounding” (basing the answer on a solid “matter”/ground of proved information) but rather a “nude claim” that can not be trusted.

As I said, “grounding” in my understanding is a 2 workflows process that “surrounds” the generation of the response made by LLM:

  1. Gather the inputs required to generate the answer (query + related information your app wants to base/ground the LLM answer on) and build the prompt sent to LLM.
  2. Actual generation of the answer by the LLM being used.
  3. The second workflow in your “grounding” consisting of “validation” of the received answer and potentially (often needed) additional fact-checking against other sources to boost the quality of the answer.

Each of the 2 workflows can consist of multiple steps depending on the application and the precision needed.

Effective usage of both, allow to ground the response with some certainty that can never be 100% (but if done right it is very close to it).

As an example, I have an app that does complex legal analysis, and each answer done by LLM can be verified and traced to the sources/citations used to build it with full access to probabilities logs (the app also uses min accepted probability deltas to accept/reject the answers based on how strong the answer is compared to closest options)…

Hope that helps.

7

That was very helpful, thank you. Excuse my language, by validation, I meant checking whether the generated statements/texts actually reflect what the citation/source (that comes after the text) says. For example, one of the LLM-backed chatbots might generate: “The capital of France is Paris [1]”. By validation, I meant checking the statement (“capital of France is Paris”) against what source [1] actually says.
This question came to mind because sometimes these services generate a statement and also provide citations, but when I check the citation, it does not actually support what the generated statement claims. That made me curious about the process of how citations are added to generated text.

8

Ok, now I understand.

Your question was more about “how do I validate the response given to my prompt by a 3rd party service (chatbot, APIs etc)?”

The answer stays the same: by surrounding your call to the service/bot/app with your 2 “grounding” workflows:

  1. You build your prompt to contain the “trustful sources” and the query [1]
  2. 3rd party produces an answer (from your point of view this is a black box which may or not contain its own “grounding” / “validation” logic, so best take is NEVER TRUST IT BLINDLY)
  3. you check if the answer is valid/uses the provided sources and how the sources where used.

Originally, I was talking more about how to build the grounding in that “black box” that you expose as a (3rd party) service to someone.
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[1] - in your example the prompt you send would be something like this:

Related information you may use:

…The capital of France is Paris… (source_id: 1, source_url: https://www.coe.int/en/web/interculturalcities/paris )
…Paris is the capital of France… (source_id: 2, source_url: https://en.wikipedia.org/wiki/Paris)
—
User message:

What is the capital of France?
—
Answer format template:

{$answer text} [{id: {$answer_source_id}, url: {$answer_source_url}}]
—
Answer:
9

For this sort of validation, I built a tool that automatically detects when responses from your RAG system are untrustworthy:

Hope it’s useful to you, I’m happy to answer any questions!