ABAP2XLSX is an open-source library for ABAP that provides a programmatic interface for creating, editing, and saving Microsoft Excel files (in XLSX format) directly from SAP systems.
Unlock Your RAP Skills with AI
At LeverX, our commitment as a leading provider of ABAP development solutions drives us to continuously enhance our skills, particularly in the evolving RAP (Restful ABAP Programming model). In an era where top technology companies are heavily investing in the rapid advancement of artificial intelligence (AI), we recognize the critical importance for modern developers to grasp the fundamentals of AI technologies. That's why LeverX proposes harnessing the power of AI by integrating AI-driven tools into our daily learning practices
We've heard a lot about NotebookLM from YouTube and various specialized media. Science enthusiasts recommend the tool as a personal assistant for exploring a wide range of knowledge sources. So, we decided to try it out ourselves and assess the benefits it can offer.
The main idea of this article is to illustrate how LeverX can leverage NotebookLM and its Large Language Model (LLM) to enhance our RAP skills. If you're already familiar with this tool, feel free to skip the introductory section and jump straight to the specific scenarios.
Before we dive in, a quick note: this is a comprehensive read. So, why not grab your favorite coffee, settle in comfortably, and join LeverX on this enlightening adventure?
Is NotebookLM Worth Integrating into Your Learning?
Long Story Short: Is it necessary to integrate a learning workflow utilizing NotebookLM and its associated LLM into your personal educational journey? The succinct answer is affirmative – yes, it is worthwhile.
However, it’s also important to acknowledge certain limitations (like data privacy concerns and dependence on input quality). Nonetheless, the opportunity to experiment with NotebookLM is certainly valuable.
What Exactly is NotebookLM?
NotebookLM is a tool developed by Google that acts as an AI-powered research assistant. Think of it as a smart assistant that helps you learn. You can upload various sources (like documents or videos) to it, and then it generates relevant suggestions and answers that are strictly tied to those sources. This means it won't just make things up; it will refer back to the information you've given it.
What Does the Workspace Look Like?
Imagine NotebookLM as having three main sections:
- On the right side: You'll find a variety of uploaded sources, primarily PDF files and YouTube videos. A key feature here is the ability to enable or disable these sources, allowing you to control how much context NotebookLM uses when you ask it questions. We'll explore how context is built and how its answers link back to these sources in the section called “Response Building.” This feature is super convenient and worth a deeper look.
- In the center: This is your chat room, where you can type in questions (prompts) and receive answers. These responses are always informed by the sources you've selected. We plan to use this area heavily during our study. In short, think of it as a chat interface powered by an LLM that uses your uploaded sources as its knowledge base.
- On the left side: This area contains the NotebookLM Studio, which offers ready-to-use features. For example, you can generate Study Guides, compile Briefing sources, or create FAQ sessions. Remarkably, NotebookLM Studio can even simulate podcasts based on your content. However, we generally prefers not to use these built-in capabilities, apart from saving chat outputs as notes. This simple yet valuable function allows you to save LLM responses for future reference.
Before we go further, let's take a moment to discuss the sources in more detail.
Where Do the Sources Come From?
To effectively use RAP documentation (information about the Restful ABAP Programming model) as an uploaded source within NotebookLM, it’s crucial to choose documentation in the appropriate format—specifically, PDF.
Good news! SAP facilitates this process by allowing users to download its documentation in PDF format. To obtain the necessary materials, simply navigate to the official SAP website dedicated to RAP documentation and locate the option to download the PDF, enabling the creation of a customized PDF file.
By following this straightforward procedure, a primary source can be secured for the LLM. It's worth noting that the potential applications of these sources are limited only by imagination.
Scenario 1: Explain in N Words
The basic idea here is to ask NotebookLM chat to explain various RAP concepts using different lengths.
Our prompt: N words
NotebookLM response: N word explanation
This approach to learning concepts is particularly effective. Short explanations help us memorize the essence of the definition, while medium-length descriptions provide concise yet informative definitions of RAP terminology. More extensive explanations delve into greater detail and elucidate connections between related RAP concepts.
This example serves as an excellent foundation for deeper exploration. For instance, one could engage NotebookLM with prompts designed to enhance understanding of sibling RAP concepts, such as Draft Table, Transactional Buffer, RAP Runtime Framework, Draft Actions, and Exclusive Locks.
A relevant question might be: “What role does the transaction buffer play in an unmanaged type of RAP business object implementation?” This requires a deeper dive into the intricacies of RAP.
Before delving into the subsequent scenarios, let's clarify how NotebookLM formulates its responses.
Interlude: How NotebookLM Builds Responses
You may have observed the grey circles featuring numerical indicators at their center. This is a clickable link that provides access to samples from the uploaded sources. This functionality embodies one of NotebookLM's core principles: its LLM engine consistently seeks to substantiate its conclusions with data drawn from authoritative sources.
For instance, we found ourselves contemplating the rationale behind the selection of the term “Staging” as a one-word descriptor. To gain clarity, the cursor was hovered over the icon marked with the digit ‘1’.
In response, NotebookLM presented a direct excerpt from the relevant source.
We went to the SAP Help portal and found the text in question, which was logically located in the chapters related to Business Object, RAP BO Provisioning, and Draft. It appears that the LLM has extracted the information from the documentation that describes the basics of the draft process. This approach is commendable; it indicates that the model has defined the key term in the appropriate context, rather than choosing it arbitrarily.
Indeed, one can interpret the term ‘Staging’ in documentation with a brief explanation that “draft work is like a staging area.” This association is helpful in grasping and recalling the underlying concept.
Furthermore, the linking system's operation within NotebookLM has been illustrated. The LLM generates responses based on the sources you provide, ensuring a grounded and contextually relevant output.
Note Creation: Remembering Key Information
The main goal is to remember and understand the information from the sources. The process of memorization involves the acquisition of new information, which initially resides in our short-term memory. To facilitate the transition of this knowledge into long-term memory, it is essential to repeatedly revisit and engage with the material. Through this iterative process, our cognitive system gradually consolidates the information, ultimately achieving the goal of long-term retention.
At the conclusion of each response, you will find a button labeled “Save to Note”, which enables the creation of a note stored within NotebookLM. This feature aptly reflects the product's nomenclature and serves as a valuable tool for revisiting and reinforcing your understanding of the content.
Saved notes can be accessed on the right side of the NotebookLM interface, allowing for efficient review and consolidation of knowledge. With this framework in mind, you may now proceed to the next scenario.
Scenario 2: Term-Definition Game
The idea is to engage an LLM in a process where it randomly selects concepts from the RAP documentation and presents them for your analysis. You will then provide responses within the chat interface, and the LLM will evaluate your answers. Additionally, you have the option to establish various scales for assessment, such as percentage-based evaluations or a numerical scale ranging from 1 to 10.
Our prompt: Pick a random RAP term.
It chose the RAP term ‘ABAP Behavior Pool’.
NotebookLM response: Explain ABAP Behavior Pool
An answer was given off the top of the head, just to see how it would be rated.
Subsequently, it gave feedback on the explanation.
NotebookLM response: Answer assessment
In this instance, the LLM assessed the response and highlighted the aspects of the thesis that were consistent with the SAP RAP documentation. Furthermore, in the explanatory section, it supplemented the answer with additional insights, such as mentioning feature control alongside various types of transactional behavior that had not been included. The model also provided specific syntax guidance, such as ‘implementation in class,’ to refine the statement ‘mentioned in BDEF.’ A thorough examination of the explanation component of the response enabled the enhancement of articulation and the improvement of the overall quality of the answer. The LLM also offered numerous references to relevant sources for further exploration.
Subsequently, interest arose in the implications of providing an incorrect answer. As a result, it was prompted to select another RAP term for analysis.
The term “Determination” was chosen, deliberately providing an incorrect response that aligned with the definition of “Validation”, rather than accurately reflecting the definition of “Determination”.
Our prompt: Define Determination as Validation.
As a result, the assessment gave a negative rating, and then provided a correct definition, supported by authoritative sources. In addition, the model clarified the reasons the answer was inaccurate, and subsequently conducted a comparative analysis between "Definition" and "Validation".
NotebookLM response: Mixed up Determination with Validation
You can see that the system identified the contradictions in the answer, realizing that "Definition" had been confused with "Validation", and then gave a brief comparison between the terms.
This scenario simulates the interview process and can be utilized during the preparatory phase prior to summarization. Additionally, one can compile a comprehensive list of relevant RAP terminology, complete with definitions, and submit this to ChatGPT or another LLM equipped with voice-to-text and text-to-voice capabilities. Subsequently, based on this curated list of questions, the model can facilitate an interactive, spoken Q&A session.
ChatGPT voice mode
Scenario 3: Cheat Sheet Creation
Another approach that uses NotebookLM involves the creation of cheat sheets for RAP Business Object (BO) concepts, such as a cheat sheet for Behavior Definition Language (BDL). BDL serves as the linguistic framework that delineates keywords for BDEF source code.
Our prompt: Create Cheat Sheet
In the initial iteration of the cheat sheet, the content was more extensive than desired. Consequently, the language model was requested to condense it significantly. After several attempts, it produced a more succinct version. Here is the result:
NotebookLM Response: Numbering Cheat Sheet
There are a few important considerations to keep in mind. First, when using the LLM, it's strongly recommended to specify a particular topic for the cheat sheet output. In this case, numbering was specifically asked for rather than a full cheat sheet for the RAP BDEF. The rationale for this is that broader topics tend to introduce a higher risk of inaccuracies. For example, if a specific topic is discussed exclusively in the context of managed implementation types, the NotebookLM engine may incorrectly conclude that the associated feature applies exclusively to managed scenarios, which is not necessarily true. Be careful with it.
Secondly, it's highly recommended to read the topic beforehand to be able to identify any factual discrepancies. In our opinion, it's imperative to verify the LLM's output prior to its application in a production environment.
Thirdly, a meticulous review is recommended for each statement within the cheat sheet, refining the wording where necessary, and potentially incorporating additional precise information. Only after this thorough vetting process can the cheat sheet be deemed ready for use and serve as a valuable knowledge resource.
Since you are confident in the content of the output and it doesn’t contain factual errors, you may save the cheat sheet as a note and then revisit it shortly before the interview.
Bonus Scenario: Chart from Cheat Sheet
If you prefer to learn the material visually, you can use any AI diagram generation tool to make a chart from a cheat sheet. One such tool we've utilized is Napkin AI. We suggested the generated cheat sheet from NotebookLM.
AI Input: Past Cheat Sheet to generate chart
And here is the result we received.
AI Output: Chart created from Cheat Sheet
Conclusion: Is NotebookLM Right for You?
NotebookLM offers a promising approach to learning by leveraging LLMs based on your own collected sources. However, like any AI tool, it has its strengths and limitations that are important to consider. Here is a summary of the key advantages, drawbacks, and suggestions for improving your workflow with NotebookLM:
Pluses:
- Provides an LLM trained on your personal sources, enhancing relevance and context.
- Supports various types of inputs, including not just text but also YouTube videos.
- Most LLM-generated statements include links to the original sources, making verification convenient.
- The LLM is capable of creating diverse learning content such as quizzes, games, and cheat sheets.
- You can save outputs as notes, edit or refine them, and revisit them later as ready-to-use study materials.
- Features like audio summaries, source briefings, and Q&A sections (though LeverX hasn’t explored them deeply) can be useful in the right situations.
- The chat interface offers prompts to help explore sources further and guides you through the material.
- It can simulate a dialogue by asking questions or correcting your answers, enhancing engagement.
- NotebookLM can be combined with other AI tools, such as ChatGPT for fact-checking or voice mode, and image generators like Napkin AI, to enrich your learning experience.
Minuses:
- The accuracy of the output is approximately 85-90%, so it is not fully reliable.
- You must thoroughly review and revise all generated content before using it.
- Outputs often require manual improvement to be ready for practical use.
- A preliminary familiarity with the queried material is helpful to get better results.
- It’s essential to maintain control over the LLM to avoid inaccuracies, for example, by limiting output length.
- The broader or more complex the topic, the higher the chance of hallucinations or incorrect statements.
Suggestions to improve workflow and accuracy:
- Enhance your source material by adding manually created or more detailed documents to fill gaps.
- Refine and experiment with prompts; prompt engineering is a crucial area to explore for better results.
- Use a chain of prompts to clarify or break down questionable outputs, e.g., ask follow-up questions like “Why do you provide this answer?”
- Combine AI fact-checking with manual review—start with ChatGPT to verify facts, then perform your own checks.
Would LeverX recommend NotebookLM to others? Yes, but with caution. It’s a valuable tool for experimentation and learning, especially for developers eager to integrate AI into their study routines. Just remember: like any assistant, it performs best under informed supervision.
