Build Agentic RAG using LangGraph. >Tired of your RAG system...
Rohit@rohit4verse
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Oct 28, 2025
~2 min read
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Build Agentic RAG using LangGraph.
>Tired of your RAG system failing when it gets a question that's "out of syllabus"? Traditional RAG is a fixed, one-way street, which is a major limitation. Let's talk about a much smarter approach.
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>Tired of your RAG system failing when it gets a question that's "out of syllabus"? Traditional RAG is a fixed, one-way street, which is a major limitation. Let's talk about a much smarter approach.
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>The Problem with Traditional RAG:
It follows a rigid "retrieve then generate" flow, usually from a single knowledge base. If the user's query isn't in that specific data, the system breaks. It either hallucinates, gives an irrelevant answer, or just gives up.
>The Problem with Traditional RAG:
It follows a rigid "retrieve then generate" flow, usually from a single knowledge base. If the user's query isn't in that specific data, the system breaks. It either hallucinates, gives an irrelevant answer, or just gives up.
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>How is Agentic RAG better?
Agentic RAG adds a "brain" to the process. Instead of a fixed flow, it uses an autonomous agent that can reason and decide the best course of action. It can plan, adapt its queries, and even correct its own mistakes.
>How is Agentic RAG better?
Agentic RAG adds a "brain" to the process. Instead of a fixed flow, it uses an autonomous agent that can reason and decide the best course of action. It can plan, adapt its queries, and even correct its own mistakes.
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>This agentic approach is dynamic. It can be designed to choose from multiple sources. For instance, the agent can first decide: "Is this a general question? I'll check the Q&A database. Is it about a specific product? I'll check the device manuals database."
>This agentic approach is dynamic. It can be designed to choose from multiple sources. For instance, the agent can first decide: "Is this a general question? I'll check the Q&A database. Is it about a specific product? I'll check the device manuals database."
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>The real power move is self-correction. An Agentic RAG can retrieve information and then run a check:"Is this context actually relevant to the user's query?" If the answer is "No," it can discard that context and try again by automatically routing the query to a web search.
>The real power move is self-correction. An Agentic RAG can retrieve information and then run a check:"Is this context actually relevant to the user's query?" If the answer is "No," it can discard that context and try again by automatically routing the query to a web search.
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>A simple way to build this is by using LangGraph to create a "state machine." Think of it as a flowchart for your agent's brain. You define all the possible steps (nodes) and the logic (edges) that connects them.
>A simple way to build this is by using LangGraph to create a "state machine." Think of it as a flowchart for your agent's brain. You define all the possible steps (nodes) and the logic (edges) that connects them.
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>For example, your graph would have a "Router" node to pick the source, different "Retrieve" nodes (one for each ChromaDB collection), a "Relevance_Checker" node to validate the info, and a "Web_Search" node as a fallback. LangGraph handles the flow.
>For example, your graph would have a "Router" node to pick the source, different "Retrieve" nodes (one for each ChromaDB collection), a "Relevance_Checker" node to validate the info, and a "Web_Search" node as a fallback. LangGraph handles the flow.
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>The result is a RAG system that is far more flexible, reliable, and intelligent. It can handle unexpected questions by dynamically choosing its tools and validating its own results, leading to much more accurate and grounded answers.
>The result is a RAG system that is far more flexible, reliable, and intelligent. It can handle unexpected questions by dynamically choosing its tools and validating its own results, leading to much more accurate and grounded answers.
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Wanna Learn More about it and also see the code implementation .
Refer this blog 👇🏻
ai.plainenglish.io/build-agentic-…
Refer this blog 👇🏻
ai.plainenglish.io/build-agentic-…