From RAG to Agentic Retrieval 📥🤖
Or: How to Improve AI Responses, Explained Simply
First: What the ** is RAG? 🤔📚
RAG stands for Retrieval-Augmented Generation. It’s been one of the most popular methods for improving how AI systems give answers. Rather than relying solely on the data the AI model was originally trained on, Retrieval-Augmented Generation (RAG) enables it to query internal documents, databases, and systems, providing more accurate and contextually relevant responses.
RAG combines three steps:
Retrieval: the system fetches relevant information from a database, documents, or internal systems.
Augmented: the retrieved information is added to the prompt or context, giving the model extra knowledge.
Generation: then it uses that information to generate a natural, coherent response.
This is particularly valuable in business environments, where answers must be supported by specific documents, manuals, or internal knowledge bases to ensure accuracy and prevent hallucinations or speculative content that could pose significant risks.
How does RAG work in practice? 💡
To implement a RAG system, companies start by collecting their internal data (this includes both structured data, like spreadsheets, and unstructured content, like PDFs or emails). This information is then broken down into small, readable pieces, called chunks.
Each chunk is then converted into an embedding, a numerical representation of the content, that helps the AI understand its meaning and compare it with other information. These embeddings are stored in vector databases, such as Pinecone or Chroma, so they can be quickly searched later. 🧠💾.
When someone asks the AI a question, the system looks into in the vector database to retrieve the most relevant pieces of information. These are then sent to a language model (such as GPT-4) to generate a smart, contextual response 💬✨
LlamaIndex is one of the most recognized tools for RAG, orchestrating the process of connecting AI to companies’ data to deliver smarter, more accurate answers 🚀
But as powerful as RAG is, it has limits. That’s where the next wave comes in.
Introducing: Agentic Retrieval 🌊
Traditional RAG is simple: The model receives a question, performs a single search, retrieves a set of documents, and generates a response. While effective in many cases, this approach can fall short for more complex queries.
Agentic Retrieval represents a significant advancement. It leverages AI agents that go beyond simply retrieving documents; they reason, plan, and iterate to deliver deeper, more accurate insights:
They decide which tools to use (search, APIs, databases, etc.)
They break down complex questions into smaller steps
They perform multiple retrievals if needed
They combine and synthesize the data better
Instead of acting like a search engine, they act more like a junior analyst, allowing companies to automate more tasks with greater efficiency and accuracy.
🦙LlamaIndex’s new launch:
“RAG is dead, long live agentic retrieval!”
Just this week, LlamaIndex published a major update focused on bringing agentic retrieval to the mainstream. Here's what’s new:
Agentic Retrieval Toolkit: a plug-and-play kit that helps AI decide how to find the answer.
Built-in tools: agents can now explore folders, query SQL and call APIs with no manual setup needed.
Observability features: you can trace what steps the agent took and why.
Modular & flexible: works with OpenAI, LangChain, and other ecosystems.
For non-technical users, this means you can build smarter assistants without having to orchestrate everything manually.
Who else is playing in this space?🧠✨
Agentic retrieval is quickly becoming a new standard for enterprise AI, enabling smarter, more contextual interactions.
Other companies jumping into the field are:
LangChain is a leading open-source framework for building LLM-powered applications and agentic workflows. Its key initiative, LangGraph, enables agentic retrieval, where agents reason through multi-step tasks, use memory, and coordinate tools for more accurate, context-aware outputs. LangChain has raised $35 million in funding, including a $10 million seed round led by Benchmark and a $25 million Series A led by Sequoia Capital.
AutoGen is an open-source framework by Microsoft for building multi-agent systems powered by LLMs. It enables agents to collaborate, retrieve information, use tools, and break down complex tasks. Designed for enterprise use, AutoGen supports dynamic, step-by-step reasoning beyond traditional RAG setups
Dust: Founded in 2023 by Gabriel Hubert and Stanislas Polu (formerly of OpenAI), Dust is a Paris-based startup focused on helping companies build specialized AI assistants connected to internal data sources such as Notion, Google Drive, and Slack. Backed by $16 million in Series A funding led by Sequoia Capital, Dust enables organizations to create multiple, task-specific AI agents that improve internal workflows by tailoring responses and actions to departmental needs.
Onyx, founded in 2023 in San Francisco, builds AI assistants that retrieve and process information from tools like Google Drive, Slack, and Salesforce to automate complex tasks. Its open-source platform lets companies customize agents and choose their preferred LLMs. Onyx has raised $10 million in seed funding led by Khosla Ventures and First Round Capital.
TL;DR: The pace is insane ⚡📈🔥
What felt like innovation last year is now just the baseline. RAG was the first step; agentic retrieval is next. Tools are getting more powerful, but also easier to use. 🛠️🧩
We’re going from “AI that looks up info” to “AI that actually knows how to find answers.” If you're building anything with AI and internal data, this is the moment to level up. 🧭🏗️🚀