Agentic Retrieval-Augmented Generation Pipeline
Key Points
- Retrieval‑augmented generation (RAG) improves LLM answers by pulling relevant documents from a vector database and feeding them as context to the model.
- Traditional RAG pipelines query a single database and call the LLM only once to generate a response.
- An “agenetic” RAG approach treats the LLM as an autonomous agent that can decide which of multiple vector stores to query and even choose the response format (e.g., text, chart, code) based on the query.
- By adding an agent, the system can route specific questions—such as internal policy inquiries—to a dedicated internal documentation store, while broader industry‑knowledge queries are sent to a public knowledge base, enhancing relevance and accuracy.
Sections
- Understanding Retrieval Augmented Generation - The speaker reviews the RAG pipeline, explaining how a vector database supplies context to a LLM and proposes extending the LLM’s role to meta‑tasks such as selecting which database to query and determining the appropriate response format.
- Context‑Aware Routing in Agentic RAG - The speaker explains how an LLM‑driven agent parses a user’s query, determines its context, and routes it to either internal documentation, a general knowledge database, or a failsafe response when the question falls outside the available vector stores, enabling flexible applications in support and legal tech.
Full Transcript
# Agentic Retrieval-Augmented Generation Pipeline **Source:** [https://www.youtube.com/watch?v=0z9_MhcYvcY](https://www.youtube.com/watch?v=0z9_MhcYvcY) **Duration:** 00:05:29 ## Summary - Retrieval‑augmented generation (RAG) improves LLM answers by pulling relevant documents from a vector database and feeding them as context to the model. - Traditional RAG pipelines query a single database and call the LLM only once to generate a response. - An “agenetic” RAG approach treats the LLM as an autonomous agent that can decide which of multiple vector stores to query and even choose the response format (e.g., text, chart, code) based on the query. - By adding an agent, the system can route specific questions—such as internal policy inquiries—to a dedicated internal documentation store, while broader industry‑knowledge queries are sent to a public knowledge base, enhancing relevance and accuracy. ## Sections - [00:00:00](https://www.youtube.com/watch?v=0z9_MhcYvcY&t=0s) **Understanding Retrieval Augmented Generation** - The speaker reviews the RAG pipeline, explaining how a vector database supplies context to a LLM and proposes extending the LLM’s role to meta‑tasks such as selecting which database to query and determining the appropriate response format. - [00:03:12](https://www.youtube.com/watch?v=0z9_MhcYvcY&t=192s) **Context‑Aware Routing in Agentic RAG** - The speaker explains how an LLM‑driven agent parses a user’s query, determines its context, and routes it to either internal documentation, a general knowledge database, or a failsafe response when the question falls outside the available vector stores, enabling flexible applications in support and legal tech. ## Full Transcript
So we all know what retrieval augmented generation is.
But let's just do a quick refresher.
Retrieval augmented generation is a powerful and popular
pipeline that enhances responses from a large language model.
It does this by incorporating relevant data retrieved from a vector database,
adding it as context to the prompt, and sending it to the LLM for generation.
What this does is it allows the LLM to ground its response in concrete and accurate information,
and that improves the quality and reliability of the response.
Let me quickly sketch it out.
So let's say we have a user
or an application, even.
And they send a query.
Now without retrieval augment the generation.
This query is going to go and get itself interpolated into a prompt.
And from there
that's going to hit the LLM.
And that's going to generate an output.
To make this rag.
We can add a vector database.
So instead of just going directly and getting itself
interpolated into the prompt, it's going to hit this vector db.
And the response from that vector db is going to be used as context
for the prompt.
Now in this typical pipeline we call the LLM only once,
and we use it solely to generate a response.
But what if we could leverage the LLM not just for responses,
but also for additional tasks like deciding which vector database to query
If we have multiple databases, or even determining the type of response to give?
Should an answer with text generate a chart or even provide a code snippet?
And that would all be dependent on the context of that query.
So this is where the agenetic RAG pipeline
comes into play.
In agenetic RAG, we use the LLM as an agent and the LLM goes beyond just generating a response.
It takes on an active role and can make decisions that will improve
both the relevance and accuracy of the retrieved data.
Now, let's explore how we can augment the initial process
with an agent and a couple of different sources of data.
So instead of just one single source,
let's add a second.
And the first one can be, you know,
internal documentation, Right?
And the second one can be general industry knowledge.
Now in the internal documentation we're going to have things
like policies procedures and guidelines.
And the general knowledge base
will have things like industry standards, best practices and public resources.
So how can we get the LLM to use the vector database
that contains the data that would be most relevant to the query?
Let's add that agent into this pipeline.
Now, this agent can intelligently decide which database
to query based on the user's question, and the agent isn't making a random guess.
It's leveraging the LLMs language, understanding capabilities
to interpret the query and determine its context.
So if an employee asks what's the company's policy on remote work
during the holidays, it would route that to the internal documentation,
and that response will be used as context for the prompt.
But if the question is more general, like what are the industries standards
for remote work in tech companies,
the agent's going to route that to the general knowledge database,
and that context is going to be used within that prompt powered by an LLM
and properly trained, the agent analyzes the query and based on the understanding
of the content and the context, decides which database to use.
But they're not always going to ask questions that are generally
or genuinely relevant to any of this, any of the stuff that we have
in our vector DB.
So what if someone asks a question that is just totally out of left field?
Like who won the World Series in 2015?
What the agent can do at that point is it could route it to a failsafe.
So because the agent is able
to recognize the context of the query,
it could recognize that it's not a part of the two databases that we have,
could route it to the failsafe and return back.
Sorry, I don't have the information in looking for.
This agentic RAG pipeline can be used in customer support systems and legal tech.
For example, a lawyer can source
answers to their questions from like their internal briefs
and then in another query, just get stuff from public caseload databases.
The agent can be utilized in a ton of ways.
Agentic RAG is an evolution in how we enhance the RAG pipeline by moving
beyond simple response generation to more intelligent decision making.
By allowing an agent to choose the best data sources
and potentially even incorporate external information
like real timedata or third party services.
We can create a pipeline that's more responsive, more accurate, and more adaptable.
This approach opens up so many possibilities for applications
in customer service, legal, tech, health care,
virtually any field as IT technology continues to evolve.
We will see AI systems that truly understand context
and can deliver amazing values to the end user.