Understanding & Building LLM Applications!

Large Language Models (LLMs) represent a significant advancement in the field of artificial intelligence, particularly in understanding and generating human-like text.?

LLMs are a specific type of deep learning model trained on massive datasets of text and code. These models possess extensive knowledge and understanding of language, allowing them to perform various tasks, including:

• Text generation. Create realistic and engaging text like poems, code snippets, scripts and news articles.
• Translation. Convert text from one language to another — accurately and fluently.
• Question answering. Provide informative, comprehensive answers to open-ended and challenging questions.
• Summarization. Condense large amounts of text into concise and informative summaries.

How do LLMs work?

LLMs typically utilize Transformer-based architectures we talked about before, relying on the concept of attention. This allows the model to focus on relevant parts of the input text when making predictions and generating outputs.

The training process involves feeding the LLM massive datasets of text and code. This data helps the model learn complex relationships between words and phrases, ultimately enabling it to understand and manipulate language in sophisticated ways.

Generative AI models have become increasingly sophisticated and diverse, each with its unique capabilities and applications.

Some notable examples include:

• Gemini?

Gemini (formerly Bard) is a Google AI chatbot that uses natural language processing to chat naturally and answer your questions. It can enhance Google searches and be integrated into various platforms, providing realistic language interactions.

• ChatGPT?

Developed by OpenAI, ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) model, specifically fine-tuned for conversational responses. It's designed to generate human-like text based on the input it receives, making it useful for a wide range of applications including customer service and content creation. ChatGPT can answer questions, simulate dialogues and even write creative content.

• DALL-E?

Also from OpenAI, DALL-E is a neural network-based image generation model. It's capable of creating images from textual descriptions, showcasing an impressive ability to understand and visualize concepts from a simple text prompt. For example, DALL-E can generate images of "a two-headed flamingo" or "a teddy bear playing a guitar," even though these scenes are unlikely to be found in the training data.

• MidJourney?

Midjourney is a generative AI tool that creates images from text descriptions, or prompts. It's a closed-source, self-funded tool that uses language and diffusion models to create lifelike images.

• Stable Diffusion:?

Stable Diffusion is a deep learning model that uses diffusion processes to create high-quality images from input images. It's a text-to-image model that can transform a text prompt into a high-resolution image.

Each of these models represents a different facet of generative AI, showcasing the versatility and potential of these technologies. From text to images, these models push the boundaries of what's possible with AI, enabling new forms of creativity and problem solving.

Components of an LLM

LLMs are built upon three foundational pillars: data, architecture and training.

• Data. This is the cornerstone of any LLM. The quality, diversity and size of the dataset determine the model's capabilities and limitations. The data usually consists of a vast array of text from books, websites, articles and other written sources. This extensive collection helps the model learn various linguistic patterns, styles and breadth of human knowledge.

• Architecture. This refers to the underlying structure of the model, often based on a Transformer architecture. Transformers are a type of neural network particularly well-suited for processing sequential data like text — using mechanisms like attention to weigh the importance of different parts of the input data. This architecture enables the model to understand and generate coherent, contextually relevant text.

• Training. The final piece is the training process, where the model is taught using collected data. During training, the model iteratively adjusts its internal parameters to minimize prediction errors. This is often done through supervised learning, where the model is given input data along with the correct output and learns to produce the desired output on its own. The training process is not only about learning the language but also about understanding nuances, context and the ability to generate creative or novel responses.

Each of these components plays a crucial role in shaping the capabilities and performance of a Large Language Model. The harmonious integration of these elements allows the model to understand and generate human-like text, answering questions, writing stories, translating languages and much more.

How do LLMs learn???

It is very important to understand how these LLMs are trained, so let’s take a closer look at the training process:?

• Input. The model begins with a vast and diverse corpus of text data sourced from books, Wikimedia, research papers and various internet sites. This data provides the raw material for the model to learn language patterns.

• Tokenize. In this step the text is tokenized, meaning it is divided into smaller pieces — often words or subwords. Tokenization is essential for the model to process and understand individual elements of the input text.

• Token embeddings. Each token is transformed into a numerical representation known as a token embedding. These embeddings are vectors that encode the meaning of the tokens in a way that words with similar meanings are closer together in the vector space.

• Encoding. The embeddings are then passed through the encoding layers of the transformer model. These layers process the embeddings to understand the context of each word within the sentence. The model does this by adjusting the embeddings in a way that incorporates information from surrounding words using self-attention mechanisms.

• Pre-trained Transformer model.: The main architecture of the model is a Transformer, which has been pre-trained on the input data. It has learned to predict parts of the text from other parts, adjusting its internal parameters to minimize the prediction error. This model has layers of attention, feedforward neural networks and a large number of parameters to capture the complexities of language.

• Human feedback. This is an optional but important step where humans can provide feedback on the model's outputs, further fine-tuning the model's performance. The feedback loop can help align the model's outputs with human expectations and standards.

• Output text. When generating text, the model uses the pre-trained and possibly fine-tuned parameters to predict the next word in a sequence. This involves the decoding process.

• Decoding. The decoding step is where the model converts the processed embeddings back into human-readable text. After predicting the next word, the model decodes this prediction from its numerical representation to a word. The process of decoding often involves selecting the word with the highest probability from the model's output distribution. This prediction can be the final output or it can serve as an additional input token for the model to predict subsequent words, allowing the model to generate longer stretches of text.

This cycle of encoding and decoding, informed by both the original training data and ongoing human feedback, enables the model to produce text that is contextually relevant and syntactically correct. Ultimately, this can be used in a variety of applications including conversational AI, content creation and more.

Building an LLM application

Here are the high-level steps you need to know to build an LLM application:?

1. Focus on a single problem first. The key? Find a problem that’s the right size: one that’s focused enough so you can quickly iterate and make progress, but also big enough so that the right solution will wow users.

2. Choose the right LLM. You’re saving costs by building an LLM app with a pre-trained model, but how do you pick the right one? Here are some factors to consider:

? Licensing. If you hope to eventually sell your LLM app, you’ll need to use a model that has an API licensed for commercial use. To get you started on your search, here’s a community-sourced list of open LLMs that are licensed for commercial use.

? Model size. The size of LLMs can range from seven to 175 billion parameters — and some, like Ada, are even as small as 350 million parameters. Most LLMs (at the time of writing) range in size from 7-13 billion parameters.

3. Customize the LLM. When you train an LLM, you’re building the scaffolding and neural networks to enable deep learning. When you customize a pre-trained LLM you’re adapting the LLM to specific tasks, like generating text around a specific topic or in a particular style.?

4. Set up the app architecture. The different components you’ll need to set up your LLM app. Can be broadly divided into three categories - User input , Input enrichment & prompt construction tools and efficient and responsible AI tooling.?

5. Conduct online evaluations of your app. These are considered “online” evaluations because they assess the LLM’s performance during user interaction.?

See more on the architecture of today’s LLM applications.

Here is a list of my favorite Dev tools to build AI/ML applications.

Of which SingleStore Notebook feature and Wing programming language are the most amazing ones. With Wing, You can build any AI/ML application with minimal code. Here is a technical overview image of this example of how to use OpenAI’s API with Wing.

Limitations of LLMs?

While LLMs offer impressive capabilities in language understanding and generation, they also have limitations that can hinder their effectiveness in real-world applications. These limitations include:

• Missing context. LLMs may struggle to understand the broader context of a given task, leading to inaccurate or irrelevant outputs.

• Non-tailored outputs. LLMs may generate generic responses that don't address the specific needs or requirements of the user or task.

• Limited specialized vocabulary. LLMs trained on general datasets may lack the specialized vocabulary needed for specific domains or tasks.

• Hallucination. LLMs can sometimes invent information or generate outputs that are biased, inaccurate or misleading.

You can mitigate this hallucinated nature of LLMs using different techniques like Retrieval Augmented Generation (RAG), Prompt Engineering and Fine-Tuning.

Here is my complete guide on reducing LLM hallucinations.

Learn many Generative AI concepts in my e-book 'GenAI for Everyone'.

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