DEV Community: Michael Madumere

Why Your RAG Pipeline is Failing: The Chunk Mismatch Problem and How to Fix It

Michael Madumere — Mon, 06 Jul 2026 16:04:16 +0000

Watching your newly built Retrieval-Augmented Generation (RAG) chatbot confidently return an outdated FAQ page just because the keywords matched is a rite of passage for AI engineers.

A while ago, I experienced a specific failure mode while building a customer service agent for a client with multiple hospitality businesses, including a hotel and a beach resort. We realized that with standard vector RAG, segmenting their data into fixed-length text chunks completely disconnected the related content. As a result, the system consistently mismatched booking queries across the different entities.

This is known as the Chunk Mismatch problem. When you slice a complex document into arbitrary chunks to fit an embedding model's context window, you destroy its logical coherence. If an engineering ticket describes a bug at the top and the deployment solution at the bottom, standard text segmentation splits them apart.

Here is a technical breakdown of how to fix this by enforcing structure at write time using GraphRAG and Entity Resolution.

1. Structuring Data at Write Time: The Dual-Level Graph

To solve the chunk mismatch problem, you have to stop chunking flat text and start structuring your data before the retrieval stage.

A great architectural blueprint for this comes from LinkedIn's customer service engineering team, who abandoned flat text chunking in favor of a dual-level Knowledge Graph. They structured their historical issue tickets using two distinct layers:

The Intra-Issue Tree

Instead of breaking a ticket into arbitrary text chunks, the ingestion pipeline parses it into a tree structure. Each node represents a distinct, logical section of the ticket, such as the Summary, Description, or Steps to Reproduce. This preserves the internal logic of the document.

The Inter-Issue Graph

The system then creates a network of connections across different tickets. This includes explicit links (for example, Ticket A is CLONED_FROM Ticket B) and implicit links based on the semantic similarity between ticket embeddings.

When a user submits a query, the system extracts the entities and intents, and then navigates this highly structured graph to retrieve the exact subgraphs needed to generate a complete answer.

2. The Unspoken Prerequisite: Entity Resolution

As a fellow developer recently pointed out to me, building these interconnected graphs is completely useless if your system is littered with duplicate or conflicting entity representations.

This brings us to Entity Resolution, which is the foundational capability that reconciles overlapping or related entity representations across different systems into unified, canonical nodes.

If your ingestion pipeline treats "Hotel A", "Hotel-A-Booking", and a raw property ID as three isolated nodes, any multi-hop traversal or depth query will immediately hit a dead end or create phantom paths. The knowledge graph is only as good as the canonical entities it is built upon. You must resolve these relationships at write time so the LLM has a single, verifiable source of truth to reason over.

3. Advanced Mechanics: URIs over String Labels

For developers looking to architect enterprise-grade semantic layers, how you define those resolved entities matters immensely.

Simple Labeled Property Graphs (LPGs) often rely on basic string labels to identify nodes and relationships. However, basic string labels often lack the rich, unambiguous meaning required for complex data integration.

Advanced enterprise semantic architectures based on the Resource Description Framework (RDF) solve this by using specific URIs (Uniform Resource Identifiers) and IRIs (Internationalized Resource Identifiers) to establish absolute semantic clarity. By enforcing identity through URIs rather than simple text strings, you guarantee that no matter where the data originates, your graph traversal understands exactly which canonical entity it is referencing. This eliminates the phantom paths that derail your LLM and provides the deep context essential for accurate retrieval.

Wrapping Up

If you are tired of LLM hallucinations and want to move beyond basic API wrappers, you have to look at how your data is ingested. By moving away from flat text chunks, utilizing dual-level graphs, and strictly enforcing entity resolution at write time, you can build AI systems that actually understand your business logic.

Have you run into the chunk mismatch problem in your own RAG pipelines? Let me know how you handled your data structuring in the comments!

Understanding Multi-Hop Reasoning: How Graph Databases Traverse Data for AI

Michael Madumere — Thu, 02 Jul 2026 10:33:51 +0000

Vector databases are highly effective for semantic search, but they often struggle when an application requires multi-step reasoning across people, systems, and events. Standard vector Retrieval-Augmented Generation (RAG) retrieves isolated chunks of text based on mathematical distance, meaning it frequently fails to capture the structured, relational context between data points.

To solve this, developers are turning to GraphRAG, which connects Large Language Models (LLMs) to a live knowledge graph that can be queried deterministically.

Here is a technical breakdown of how graph databases physically traverse data to supply LLMs with explicit, highly relevant connections, and how you can configure AI agents to control this retrieval.

1. Multi-Hop Reasoning (The Architecture of a "Hop")

In a Text-Attributed Graph, data is stored as nodes (entities like users, products, or tickets) connected by edges (relationships like "IMPACTS" or "CLONED_FROM"). A "hop" is the traversal from one node to another via an edge.

Multi-hop reasoning is the process of capturing a sequence of these relationships across multiple interconnected nodes. While standard RAG looks for a single text chunk containing an answer, multi-hop traversal allows the system to extract indirect connections that are never explicitly written together in a single document.

The Customer Support Chatbot Example:
Imagine a developer is building an internal chatbot to help customer support agents troubleshoot issues. An agent asks the chatbot: "How do I resolve the CSV upload error for user emails?"

If the developer used standard vector RAG, the system would simply perform a semantic search for the keywords "CSV upload error" and "user email". It would likely return the very first result that matches that text, which could be an outdated FAQ page, a completely unrelated bug report, or a generic troubleshooting guide. Standard RAG matches the words but misses the business context.

A multi-hop GraphRAG system, however, explicitly verifies the exact sequence of events. It traces the relationships across user profiles, historical bug tickets, and process logs to give a concrete, source-backed explanation. The graph database might traverse the following path:
(Node: User Bug Report) -> [Edge: CLONED_FROM] -> (Node: Master Engineering Ticket) -> [Edge: HAS_RESOLUTION] -> (Node: Patch Deployed).

This ensures the LLM receives the exact, verified resolution steps rather than a loose semantic match.

2. The Depth Lever (Vertical Traversal)

Depth is a query mechanism used when an AI needs to drill down vertically through layers of relationships to reach a highly specific target. This is the exact programmatic lever used for automated root-cause analysis.

In graph query languages like Cypher (used by Neo4j and Memgraph), developers control depth by specifying a range for the number of hops the database should traverse away from the starting node.

// Example: Deep traversal to find reproduction steps for a specific bug ticket
MATCH (j:Ticket {ticket_ID: 'ENT-22970'})-[:HAS_DESCRIPTION*1..5]->(desc:Description)-[:HAS_STEPS_TO_REPRODUCE*1..5]->(steps:StepsToReproduce)
RETURN steps.value

How it works: The *1..5 parameter is the depth lever. It instructs the database engine to traverse a minimum of 1 and a maximum of 5 relationship layers away from the starting ticket node to find the specific target nodes. In our customer support example, this allows the system to navigate deep, multi-tiered dependencies from the initial ticket description down to the exact reproduction steps, a path that a vector search would completely miss.

3. The Breadth Lever (Horizontal Traversal)

Breadth traversal is used when a query requires a broad overview or an understanding of an entire ecosystem. Instead of drilling down a single chain of events, breadth expands the search horizontally to explore the immediate connections directly surrounding a starting node.

By looking at how many different nodes, or specific types of nodes, a central entity is connected to, developers can retrieve a complete horizontal view of a scenario.

// Example: Breadth traversal to pull all active support tickets impacting a product line
MATCH (p:Product {name: 'Enterprise Dashboard'})-[:IMPACTED_BY]->(t:SupportTicket)
RETURN t.id, t.status

How it works: Instead of traversing deep chains, this query pulls in all parallel nodes of the type SupportTicket that are directly connected to the Enterprise Dashboard node. For a customer support chatbot, this allows the LLM to process a broad set of adjacent concepts simultaneously, such as server outages, user login bugs, and billing errors that are all impacting the same product line at that exact moment.

4. Configuring the Agentic Workflow

For developers building advanced, autonomous RAG pipelines, breadth and depth should not be hard-coded static queries. Instead, they are designed as dynamic levers controlled by a multi-agent system.

To build this architecture, you need to configure two specific agent roles:

The Coordinator Agent: This agent acts as the query planner. It evaluates the user's natural language prompt to determine the exact balance of breadth and depth required to answer the question. The agent maps its evaluation to a discrete mathematical range (e.g., a float between 0.0 and 1.0) and injects that parameter into the graph query generation. For instance, a broad research question regarding overall platform stability scores a high breadth parameter, triggering a wide horizontal search. Meanwhile, a specific troubleshooting question about a single delayed order scores a high depth parameter.
The Recursive Retrieval Agent: As the database executes the query and traverses horizontally or vertically, this agent continuously evaluates the newly discovered nodes. It dynamically decides what contextual data to keep and what to prune before the final graph extraction is formatted as a node sequence or adjacency table and passed to the generator LLM.

By wiring these agents to control graph database traversal, you build a system that dynamically scales its investigation scope, resulting in highly deterministic, complete answers that standard vector similarity simply cannot achieve.

Rethinking Enterprise AI: Why GraphRAG is the Missing Link

Michael Madumere — Thu, 02 Jul 2026 09:07:41 +0000

Part 1: Social Listening

I was reading a paper called "A Graph RAG Approach to Enhance Explainability in Dataset Discovery." It made something click.

The paper describes how combining LLMs with Knowledge Graphs helps analysts find datasets by generating explanations that justify why a result was chosen. The researchers applied this to data management, but the architecture made me realize how many business AI systems hit the same wall. They rely on standard generative AI and vector search. That works fine for reading text. It falls apart on complex enterprise relationships.

I am starting a series on specific business areas where GraphRAG beats traditional RAG. First up: Social Listening and Market Context.

The problem: keyword counting vs. real context

Most brands spend heavily on tools that monitor online discussions and market variables. These tools often work like keyword counters with better marketing. They tell you a conversation is happening. They do not explain why.

Standard RAG cannot fix this because it retrieves information based on semantic similarity. If a PR team asks an AI why brand sentiment dropped, a standard system might return text chunks that mention the brand next to "angry." No timeline. No chain of events. Just matches.

The GraphRAG solution: social chatter as a knowledge graph

What if instead of storing social media posts as flat text, you used LLMs to pull out the entities and relationships and structured that noisy data into a Knowledge Graph? Evaluating data as a web of relationships gives you three things that flat search cannot.

Multi-hop reasoning finds the why

Instead of matching keywords, a graph connects the dots. The system traces a path: Influencer A criticized Feature B, which triggered Trending Topic C. Those are real causal links. Marketing teams can run automated root cause analysis, tracing outcomes back through upstream events to see where a narrative started.

Breadth control for market research

Business questions mix deep details and broad overviews. Graph databases give you programmatic levers. Breadth traversal expands outward from a single node (a brand) to see adjacent connections. A system can pull in parallel market conditions from consumer complaints on X to macroeconomic news. You get the full picture, not a narrow list of mentions.

Explainable AI builds trust

The original paper notes that explainability matters for transparency and trust. Because GraphRAG retrieves specific relationship paths, the LLM can generate explanations that state the data trail it followed. It is not a black box guess. The AI can show you exactly how it reached its conclusion.

What is next

By shifting from "searching for text" to "navigating relationships," we can build AI systems that understand business logic and causality.

I will continue this series over the next few weeks, exploring how GraphRAG transforms other industries. I will also build open-source proof-of-concept tools alongside these posts. Up first is the repository for the GraphRAG Social Listening platform.

If you want to move beyond basic AI wrappers, follow along.

What is web 3? How is it different from web 2?

Michael Madumere — Wed, 02 Mar 2022 05:48:56 +0000

Introduction

Since the creation of the internet, web technologies have constantly evolved, leading to an outburst of innovation and growth in the tech community. Web 3 is the latest idea and vision of the future of the internet, in which people are in full control of their personal data and choose who gets access to that information.

Web 2

Web 2 is mainly described as the generation of the internet we are primarily exposed to now, where users exchange their data for value from tech companies around the world. It is referred to as the read/write web, because users can interact with web applications by posting content and can also consume content.

Web 3

Web 3 is an idea for the next generation of the internet where data would be interconnected in a decentralized manner, ensuring that no single organization has autonomy over the information spread across the internet.

Major differences between web2 and web 3

The major difference between Web 3 and Web 2 would be how data is distributed and stored across the internet. Here are some of the major differences I found on the internet:

Web 3 is built around decentralization of data while in Web 2, data is centralized within tech giants.
In web 3, data is stored on a decentralized ledger known as a blockchain, whereas, tech companies keep information on their private servers and databases in web 2.
In web 2, companies exchange value by sharing users data to third party vendors, however, web 3 plans to break down this barrier by allowing users to own their data and subsequently monetize them.
Since users are in control of their data in web 3, information censorship would be limited, since no single organization can independently restrict users access without permission from the user. This however, is a major concern as criminals can exploit this freedom to promote illegal activities on the internet.
Web 3 companies are run by DAOs (Decentralized Autonomous Organization) which are organizations run by members of the public that own a stake in a decentralized project, either by owning tokens from the project or other forms of value determined by the community. In contrast, web 2 companies are run by shareholders and can be easily influenced by the decisions of the centralized body.

Conclusion

The concept of web 3 is relatively new with a lot of questions still unanswered, there is still a lot to uncover, which makes it even more exciting as we explore the numerous possibilities.
Every new advancement comes with its perks and pitfalls especially in the early days, but as more people understand the possibilities and potentials, this leads to more stability and ultimately mainstream adoption.

There are numerous opportunities available in the web 3 space but you need to acquire specific skills and understand how the system works. Learning resources are not as abundant as other fields, but a few organizations are taking the initiative to train people to become experts in this field. Block Games is an excellent opportunity to learn Web 3 skills, it is organized by the Zuri team and sponsored by Nestcoin.