This post details Large Language Models (LLMs), covering their Transformer architecture, core limitations like hallucinations, and advanced customization methods like Fine-Tuning and RAG. It also provides a practical guide on building a Retrieval-Augmented Generation (RAG) application using Podman Desktop & Podman AI Lab.

Introduction to Large Language Models (LLMs)

A Large Language Model (LLM) is an AI program designed to recognize and generate human-like text, among other tasks. LLMs earn the “large” designation because they are trained on massive datasets, often comprising thousands or millions of gigabytes of text scraped from the Internet.

LLMs fall under the broader umbrella of Machine Learning (ML) and Deep Learning (DL). Machine Learning is a field of AI focused on developing statistical algorithms that learn from data and can generalize to unseen data without explicit programming. And Deep Learning is a specialized branch of ML that utilizes multi-layer neural networks.

Because LLMs create new content (text), they are also categorized as a form of Generative Artificial Intelligence (GenAI). GenAI involves the use of deep neural networks to produce various types of content, including text, images, audio, and synthetic media.

LLM Architecture Fundamentals

The backbone of modern LLMs is the Transformer architecture. This deep neural network is essential for parsing and generating human-like text and consists of two main components:

• Encoder: Processes the input text to create a numerical embedding.
• Decoder: Generates translated or output text sequentially.

Before processing, raw text must be converted into a format a neural network can understand. This involves two key steps:

• Word Embeddings: The concept of converting raw data (words) into continuous-valued, dense vector representations, which are mathematically compatible with neural networks.
• Tokenization: The fundamental process of breaking down text into individual units, or “tokens,” for processing. A sophisticated scheme like Byte Pair Encoding (BPE) is often used to efficiently manage a vast vocabulary, effectively merging frequent pairs of characters until a predefined vocabulary size is met.

A critical limitation is the context length, which refers to the maximum number of tokens the model can process in a single input.

## Limitations of LLMs Despite their power, LLMs have notable drawbacks:

• Reliability: The output quality is dependent on the quality of the ingested training data.
• Hallucination: LLMs can generate fake or inaccurate information when they lack accurate answers.
• Vulnerability: LLM-based applications are susceptible to bugs and malicious inputs, potentially leading to dangerous or unethical responses.
• Data Privacy: Users risk exposing confidential data, as LLMs may use inputs to train their models and are not designed as secure data vaults.

Advanced Techniques for Customizing LLMs

Customization is key to turning a generic LLM into a powerful, specialized tool. The most advanced techniques include:

Technique	Technique
Fine-Tuning	Adapting a general-purpose model to a specific task or dataset by making small adjustments to its parameters.
Retrieval-Augmented Generation (RAG)	Enhancing LLMs with external, up-to-date contextual knowledge (from a database) to create domain-specific applications, bypassing the static nature of the model’s training data.
Prompt Engineering	The art of crafting effective inputs (prompts) to guide the LLM toward a desired output.
Prompt Tuning	An automated approach that uses optimization techniques to find the best prompt parameters for a given task.
Parameter-Efficient Fine-Tuning (PEFT)	Techniques that efficiently fine-tune large models by modifying only a small subset of the model’s parameters, reducing computational costs.
Quantization	Reducing the computational and memory requirements of an LLM by converting its weights from high-precision (e.g., 32-bit floating point) to lower-precision formats (e.g., 8-bit integers).
Reinforcement Learning with Human Feedback (RLHF)	Fine-tuning a pre-trained model using human feedback (ratings/rankings of outputs) to train a reward model, which then guides the LLM to align with human preferences.

# Introduction to Generative AI Generative AI (GenAI) is inspired by the human brain’s neural network, a system that learns from data and improves continuously.

## Generative Models & Architectures

Model/Architecture	Function	Example
LLM	Understands, generates, and manipulates human language.	GPT-3, BERT
Generative Adversarial Network (GAN)	Creates new data (like realistic images/text) using two competing neural networks: a generator and a discriminator.	Image Generation
Convolutional Neural Network (CNN)	Used to recognize patterns in data, such as objects in images or words in text.	Image Recognition
Variational Autoencoder (VAE)	Generates new content, detects anomalies, and removes noise; suitable for signal analysis and medical imaging.	Medical Imaging Analysis
Diffusion Model	Powerful content-based image retrieval techniques; used for image search and generation.	Stable Diffusion, DALL-E 2
Autoregressive Model (AR)	A linear predictive model that uses past data to predict future trends, commonly used for time-series analysis.	Stock Market Forecasting

## Key Tools and Frameworks TensorFlow: An open-source deep learning framework by Google, known for its comprehensive ecosystem, pre-trained models (TensorFlow Hub), and integration with the user-friendly Keras API.

PyTorch: An open-source deep learning framework by Facebook’s AI Research lab, favored by researchers for its flexibility and dynamic computational graph.

GPT-3: OpenAI’s model with 175 billion parameters, setting standards in NLP with its human-like text generation.

BERT: Google’s groundbreaking NLP model that significantly advanced tasks like text classification and question answering.

# A Practical Guide to Building a RAG Application

Here’s a practical, step-by-step look at how developers set up a RAG application using tools like Podman Desktop and the Podman AI Lab.

Step 1: Understand the Goal The entire process is aimed at building an AI application that’s context-aware and uses the most up-to-date information, rather than just relying on its original training data. This is essential for enterprise or specialized applications where facts change constantly.

Step 2: Set Up the Architecture To make RAG work, you need three key components connected:

The LLM (Model Service): This is the brains of the operation—the model that actually generates the text. It’s hosted on an Inference Server (often a container) ready to receive a prompt and spit out an answer.

The Knowledge Base (Vector Database): You need a place to store your custom, private, or current data. This data (like company documents, news articles, or financial reports) is converted into numerical vectors and stored in a specialized database, like Chroma DB. This setup enables semantic search, allowing the system to quickly find the most relevant document based on the meaning of the user’s question.

The Interface (AI Application Frontend): This is the part the user sees. Tools like Streamlit are often used to quickly whip up a simple web application where the user can type in their question.

Step 3: Connect and Execute the RAG Flow The tutorial demonstrates how to use the Podman AI Lab’s pre-packaged RAG Recipe to rapidly deploy this three-part architecture as connected containers.

User Asks: A user enters a question into the Streamlit Frontend.

Retrieve: The application sends the question to the Chroma DB. The Vector Database instantly identifies the most relevant text snippets from your custom data that match the question’s intent.

Augment: These retrieved snippets are appended to the original user question, creating a super-detailed, context-rich “augmented prompt.”

Generate: This augmented prompt is sent to the Model Service (LLM). The LLM now has all the context it needs to provide a highly accurate, customized, and up-to-date answer.

By following this process, developers learn how to leverage containerization (via Podman Desktop) to effortlessly deploy a complex AI workflow, giving them full control over their model and data while ensuring the AI remains accurate and relevant.

Tooling

Podman Desktop: A tool for developing, running, and managing containers on your local machine, simplifying the setup of complex AI components.

Podman AI Lab: This environment provides pre-packaged “recipes” for common AI use cases, streamlining the deployment of the necessary infrastructure.

Model Service (Inference Server): This component hosts the actual Large Language Model (LLM) that will perform the text generation.

Vector Database (Chroma DB): This database is crucial for RAG. It stores the custom data (documents, text, knowledge base) as numerical embeddings (vectors), allowing for extremely fast and accurate semantic search to find the “relevant context” for the prompt.

AI Application Frontend (Streamlit): a Python library, to quickly build a user-friendly web interface where a user can interact with the RAG application.

## Recipe

All you need to do to perform the above example is to install the “RAG Chatbot” recipe from Podman AI Lab and follow the instructions.

And enjoy!!