Introductions are sometimes boring, but here, it is important for us to set the context and help you familiarize yourself with GenAI and LLMs before we dive deep into LlamaIndex. I will try to be as concise as possible and, if the reader is already familiar with this information, I apologize for the brief digression.

What is GenAI?

GenAI refers to systems that are capable of generating new content such as text, images, audio, or video. Unlike more specialized AI systems that are designed for specific tasks such as image classification or speech recognition, GenAI models can create completely new assets that are often very difficult – if not impossible – to distinguish from human-created content.

These systems use machine learning (ML) techniques such as neural networks (NNs) that are trained on vast amounts of data. By learning patterns and structures within the training data, generative models can model the underlying probability distribution...

Oh! What good times we are living in. There has never been a more favorable era for small businesses and entrepreneurs. Given the enormous potential of this technology, it’s a real miracle that, instead of ending up strictly under the control of large corporations or governments, it is literally within everyone’s reach. Now, it’s truly possible for almost anyone – even a non-technical person – to realize their ideas and solve problems that until now seemed impossible to solve without a huge amount of resources.

The disruptive potential that LLMs have – in almost all industries – is enormous.

It’s true: there are concerns that this technology could replace us. However, technology’s role is to make lives easier, taking over repetitive activities. As before, we’ll likely do the same things, only much more efficiently and better with LLMs’ help. We will...

Not all the news is good, however. It’s time to also discuss the darker side of LLMs.

These models do have important limitations and some collateral effects too. Here is a list of the most important ones, but please consider it non-exhaustive. There may be others not included here, and the order is arbitrarily chosen:

• They lack access to real-time data.
  • LLMs are trained on a static dataset, meaning that the information they have is only as up to date as the data they were trained on, which might not include the latest news, scientific discoveries, or social trends.
  • This limitation can be critical when users seek real-time or recent information, as the LLMs might provide outdated or irrelevant responses. Furthermore, even if they cite data or statistics, these numbers are likely to have changed or evolved, leading to potential misinformation.

Note

While recent features introduced by OpenAI, for example, allow the underlying LLM...

Coined for the first time in a 2020 paper, Lewis, Patrick et al. (2005). “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”. arXiv:2005.11401 [cs.CL] (https://arxiv.org/abs/2005.11401), published by several researchers from Meta, RAG is a technique that combines the powers of retrieval methods and generative models to answer user questions. The idea is to first retrieve relevant information from an indexed data source containing proprietary knowledge and then use that retrieved information to generate a more informed, context-rich response using a generative model (Figure 1.5):

Figure 1.5 – A RAG model

Let’s have a look at what this means in practice:

• Much better fact retention: One of the advantages of using RAG is its ability to pull from specific data sources, which can improve fact retention. Instead of relying solely on the generative model’s own knowledge –...

In this chapter, we covered a quick introduction to GenAI and LLMs. You learned how LLMs such as GPT work and some of their capabilities and limitations. A key takeaway is that while powerful, LLMs have weaknesses – such as the potential for false information and lack of reasoning – that require mitigation techniques. We discussed RAG as one method to overcome some LLM limitations.

These lessons provide useful background on how to approach LLMs practically while being aware of their risks. At the same time, you learned the importance of techniques such as RAG to address LLMs’ potential downsides.

With this introductory foundation in place, we are now ready to dive into the next chapter where we will explore the LlamaIndex ecosystem. LlamaIndex offers an effective RAG framework to augment LLMs with indexed data for more accurate, logical outputs. Learning to leverage LlamaIndex tools will be the natural next step to harness the power of LLMs in a proficient...