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8th chunk of `content/guides/genai-leveraging-rag/index.md`
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Apache NiFi is an open-source, distributed data processing system that allows you to collect, transform, and transport data across various sources and sinks. It provides a graphical user interface for designing and managing data flows, as well as a set of APIs for integrating with other systems.
According to this Stack Overflow question, the free Neo4J JDBC driver doesn't understand SQL, only Cypher, so you would need to use a non-free BI Connector to connect to Neo4j from Apache Drill.
Additionally, this Stack Overflow question suggests that Apache POI does work in a Neo4j User Defined Function. However, there may be an unknown glitch in the standalone UDF mentioned in the initial question.
To export data from Neo4j to Excel using APOC Extended procedures, you can use apoc.export.xls.query which takes a Cypher query and exports the results to an Excel file.

References:
How to connect and query Neo4j Database on Apache Drill?
Is a Neo4j UDF compatible with Apache POI?
```

Keep in mind that new questions will be added to Stack Overflow, and due to the inherent randomness in most AI models, the answers may vary and won't be identical to those in this example.

Feel free to start over with another [Stack Overflow tag](https://stackoverflow.com/tags). To drop all data in Neo4j, you can use the following command in the Neo4j Web UI:


```cypher
MATCH (n)
DETACH DELETE n;
```

For optimal results, choose a tag that the LLM is not familiar with.


### When to leverage RAG for optimal results

Retrieval-Augmented Generation (RAG) is particularly effective in scenarios where standard Large Language Models (LLMs) fall short. The three key areas where RAG excels are knowledge limitations, business requirements, and cost efficiency. Below, we explore these aspects in more detail.

#### Overcoming knowledge limitations

LLMs are trained on a fixed dataset up until a certain point in time. This means they lack access to:

* Real-time information: LLMs do not continuously update their knowledge, so they may not be aware of recent events, newly released research, or emerging technologies.
* Specialized knowledge: Many niche subjects, proprietary frameworks, or industry-specific best practices may not be well-documented in the model’s training corpus.
Title: Leveraging RAG for Optimal LLM Performance: Knowledge Limitations, Business Requirements, and Cost Efficiency
Summary
This chunk outlines the advantages of using Retrieval-Augmented Generation (RAG) in scenarios where Large Language Models (LLMs) have limitations. RAG excels in overcoming knowledge gaps, particularly when LLMs lack real-time information or specialized knowledge due to their fixed training datasets. The chunk suggests that it is important to select a tag that the LLM is unfamiliar with for optimal results.