The rapid evolution of artificial intelligence has introduced a new vocabulary that can often obscure the underlying mechanics of these powerful systems. Two of the most foundational and frequently cited terms, parameters and tokens, are critical to understanding how large language models (LLMs) like those from OpenAI, Google, Anthropic, and DeepSeek operate. This report provides a comprehensive guide to these concepts, defines over 50 other key terms, explains the technical process of AI, and evaluates the practicality of creating a personalized AI system.

1. The Foundational Units: Parameters and Tokens

At their core, modern AI models are sophisticated systems built on two fundamental units of data: parameters and tokens. Understanding their roles is the first step toward demystifying how these platforms function.

1.1. The DNA of AI: What are Parameters?

In the simplest terms, parameters can be thought of as the “dials and settings” within a large language model that can be adjusted to optimize its performance. Just as a sound engineer manipulates the dials on a mixing board to achieve the perfect audio balance, a data scientist adjusts these internal values to refine the model’s ability to predict and generate new tokens. These parameters are the internal weights and values that an LLM learns during its training phase. They essentially act as the model’s memory, capturing complex patterns in language, such as grammar, context, and the relationships between words, over billions or trillions of examples.   

The scale of parameters in a model directly correlates with its complexity and capabilities. Models can range from just a few billion parameters to well over 1.75 trillion, and generally, a higher parameter count provides the model with a greater capacity to understand and produce nuanced and sophisticated output. However, this expansion comes with significant trade-offs that go beyond sheer size. A larger model is considerably more costly to train and run, consuming vast amounts of computational power, which in turn leads to higher energy consumption and environmental impact.   

It is important to differentiate between these learned internal parameters and the few, user-facing controls that are also sometimes called parameters. While the model’s trillion-plus parameters are fixed after training and determine its core capability, a user can adjust a handful of sampling parameters like Temperature, Max Tokens, and Top-p to control the nature of the output. For instance, a high temperature value increases the randomness and creativity of the response, making it suitable for creative writing, while a low value produces a more deterministic and focused output. This distinction is critical to a comprehensive understanding: one set of parameters defines the model’s inherent intelligence and cost, while the other set provides a means for the user to steer its creative output during the inference process.   

Another critical consideration that challenges the simple “bigger is better” notion is the quality of the training data. A smaller, more focused model trained on high-quality, curated data can often outperform a much larger model that has been trained on a lower-quality dataset. This highlights that the material an AI learns from is just as important as its size, a principle that is fundamental to the development of specialized AI systems.   

1.2. The Currency of AI: What are Tokens?

Tokens are the fundamental units of data that AI models process. They are the language and currency of AI. A token can be a word, a subword, a punctuation mark, or a sequence of characters. For example, the sentence “I heard a dog bark loudly at a cat” can be broken down into a sequence of tokens. The process of converting text into these manageable units is called tokenization and it is the essential first step in preparing data for an LLM.   

There are three common methods of tokenization, each with its own advantages and disadvantages.   

• Word tokenization: This method splits text into individual words based on a delimiter, like a space. While it results in fewer tokens for a given text, it can struggle with unknown words, typos, and can lead to a very large vocabulary.   
• Character tokenization: This method breaks text into individual characters, which allows the model to handle a wider range of inputs and can reduce memory resources. However, it results in a larger number of tokens for the same text, demanding more computational resources.   
• Subword tokenization: This is a hybrid approach that splits text into partial words or character sets. Models like OpenAI’s GPT use a form of this known as Byte-Pair Encoding (BPE), which is a balance between the other two methods. It allows the model to handle a wider range of words and typos while managing vocabulary size.   

Once a text is tokenized, the model assigns a unique ID to each token. These token IDs are then converted into multi-valued numeric vectors called embeddings. Embeddings are a critical breakthrough because they are designed to capture the semantic relationships between tokens, which means that words used in similar contexts will have similar embeddings in the vector space. This is a major improvement over older methods that simply represented each word as a numerical entry in a table without recognizing its connection to other words.   

The concept of tokens also has direct practical and financial implications. LLMs have a fixed context window—a limit on the combined number of input and output tokens that a model can process at one time. This limit determines the maximum length of a prompt and the generated response. Furthermore, many generative AI services use token-based pricing, where the cost of a request is directly tied to the number of input and output tokens. This makes tokens a literal currency in the AI ecosystem.   

2. A Lexicon of Intelligence: 50+ AI Concepts and Buzzwords

The following glossary defines more than 50 AI-related terms, organizing them into thematic categories to provide a clearer understanding of how they relate to one another.

3. The Engine of Intelligence: How Large Language Models Work

Understanding the foundational concepts of parameters and tokens leads to a more profound question: how do these models use them to work? The answer lies in the revolutionary architecture that powers all modern LLMs.

3.1. How AI Works in Layman’s Terms

At its core, a large language model is a highly sophisticated next-word predictor. It does not “understand” text in the same way a human does, but rather it uses a massive matrix of probabilities learned from its training data to predict the most likely next token in a sequence. The prompt is the input you provide, acting as a “roadmap” or “conversation starter” that guides the model’s prediction process and sets the context for the desired output. The model’s response is a continuation of that roadmap, token by token, until it reaches a defined stopping point.   

The output of this predictive process is not a single, deterministic answer. As a user, you have the ability to influence the model’s behavior by adjusting parameters that control the generation process. For instance, the Temperature parameter controls the level of randomness or creativity. A low temperature value encourages the model to stick to the most probable next token, resulting in a predictable and safe response. Conversely, a high temperature value allows the model to select from a wider range of less-probable tokens, leading to more diverse and creative outputs.   

3.2. A Technical Deep Dive into the Transformer Architecture

The technological leap that made modern LLMs possible was the introduction of the Transformer architecture in the seminal 2017 paper, “Attention is All You Need”. Before this breakthrough, previous neural network architectures like Recurrent Neural Networks (RNNs) processed text sequentially, one word at a time. This sequential processing was slow and ineffective at recognizing long-range dependencies in language, such as when a pronoun refers back to a noun many sentences earlier.   

The Transformer changed this paradigm by using a self-attention mechanism that allows it to process an entire sequence of tokens in parallel, all at once. This mechanism enables the model to weigh the significance of every word in the input relative to every other word, regardless of its position in the sequence. This ability to understand global context and dependencies across a text is what gives modern LLMs their remarkable fluency and reasoning ability.   

The process of generating a response to a prompt, known as inference, is broken down into two distinct phases. The first is the prefill phase, where the model takes the entire input (the prompt) and processes all tokens in a highly parallelized manner to compute the intermediate states needed for the first new output token. This is the part of the process that fully leverages the parallel processing power of GPUs. The second phase, the decode phase, is an autoregressive process where the model generates the output tokens one at a time. Each new token is generated based on all the preceding input and output tokens until a stopping criterion is met. This two-step process, with its shift from parallel processing of the prompt to sequential generation of the response, is the fundamental mechanism behind how LLMs work.   

For professionals, the ability to work with these systems has evolved into a new discipline known as prompt engineering. This involves not just asking questions but using a set of reproducible techniques to obtain reliable and targeted outputs. These techniques can range from zero-shot prompting (a simple instruction with no examples) to more advanced methods like Chain of Thought (CoT) prompting, which guides the model to show its step-by-step reasoning for a problem. The latter is a particularly powerful method that provides a glimpse into the “black box” of the model’s decision-making process, making the output more transparent and trustworthy. Another advanced technique is Retrieval-Augmented Generation (RAG), which enhances an LLM’s accuracy and relevance by combining it with a document retrieval system, allowing it to ground its response in a specific knowledge base.   

4. From Concept to Reality: Creating Your Own AI

The ambition to create a personal AI model, similar to a foundational model like ChatGPT, is a common goal for many enthusiasts. However, there is a vast and often misunderstood gulf between what is technologically possible for an individual and what is realistic.

4.1. Feasibility Analysis: How Hard Is It?

Training a foundational large language model from scratch is a prohibitively difficult and expensive undertaking for an individual. The costs and resources involved are astronomical. For perspective, the cost to train the original 2017 Transformer model was approximately 900 USD. In contrast, the cost to train GPT-3 (175 billion parameters) was estimated to be between 500,000 USD and 4.6 million USD, and training OpenAI’s GPT-4 reportedly cost over 100 million USD. The training of Google’s Gemini Ultra model is estimated to have cost 191 million USD in compute costs alone. The immense hardware required for such projects is equally staggering, with one model reportedly requiring 25,000 high-end GPUs over three to five months.   

The challenge is a multidimensional problem involving immense financial investment, vast computational power, and a high level of technical expertise. An individual or small team lacks the necessary data, specialized hardware, and personnel to replicate this process.   

The desire to “create my own AI” exists on a spectrum of difficulty.

1. Impossible: Training a foundational model from scratch is an undertaking reserved for well-funded organizations with access to supercomputers.
2. Feasible: Fine-tuning an existing open-source or open-weight model (such as Llama or Gemma) on a specific dataset is a realistic goal for a professional or a dedicated individual. This approach tailors a pre-trained model to a narrow task without the astronomical cost of building one from the ground up.   
3. Accessible: For the average individual, the most practical approach is to use existing AI APIs, which allow access to powerful models like GPT-4 or Gemini via simple code. Alternatively, one can build a very basic, narrow-scope model from scratch using accessible Python libraries like TensorFlow or PyTorch.   

The democratization of AI is not occurring through the ability to train from scratch but rather through the ability to leverage and refine existing, powerful models. This reframes the user’s goal from an impossible dream into an achievable and highly impactful project.

4.2. A Practical Guide to Creating a Personal AI

For a realistic and powerful AI project, fine-tuning is the most effective approach. This process involves adapting a pre-trained model to a specific task or domain, building on its existing knowledge rather than starting over.   

Here is a conceptual walkthrough of the process:

• Step 1: Define the Problem. Start by identifying a specific, narrow problem you want to solve, such as creating a chatbot for customer service or building a sentiment analysis tool for social media posts. The more specific the problem, the more successful the fine-tuning process will be.   
• Step 2: Collect and Curate Data. This is the most critical and often the most time-consuming step. High-quality, relevant data is the foundation of a successful model. A smaller, meticulously curated dataset can be far more effective than a massive, noisy one. The data must be cleaned, preprocessed, and labeled to be useful for the model.   
• Step 3: Choose Your Tools. A variety of open-source frameworks and libraries are available to simplify the process. Popular choices for deep learning and fine-tuning include TensorFlow, PyTorch, and the Hugging Face ecosystem, which is purpose-built for this kind of work and provides access to a vast library of pre-trained models and datasets.   
• Step 4: The Fine-Tuning Process. With your pre-trained model and prepared dataset, you can begin the fine-tuning process. This involves loading the model and its corresponding tokenizer, tokenizing your specific dataset, and then using a training tool to feed the new data to the model. The model’s parameters are then adjusted through a process called backpropagation to minimize the difference between its predictions and the ground-truth labels in your dataset.   
• Step 5: Evaluate and Deploy. After fine-tuning, you must evaluate the model’s performance on a separate testing dataset to ensure it can generalize to new, unseen data. Once you are satisfied, the model can be deployed, often using an API to make it available for use in a real-world application.   

4.3. Model Comparison: Platforms for the Professional

The current landscape of AI tools shows that no single model is definitively superior in every task. Instead, the most effective strategy for professionals is to view AI as a toolkit and strategically combine different models for optimal results. A comparative analysis reveals distinct strengths among the leading platforms.   

5. Conclusion: The Path Forward

The journey from understanding the most basic components of AI to comprehending its practical application reveals a cohesive and logical system. Parameters are the model’s learned intelligence, tokens are its language, and the Transformer architecture is the revolutionary engine that enables the two to work in concert.   

While the creation of a foundational model remains a near-impossible task for the individual, the accessibility of AI is rapidly expanding through the widespread availability of open-weight models and powerful APIs. The most important conclusion for the aspiring AI developer is that the future of AI is not about building a single, monolithic model from scratch. Instead, it is about the ability to strategically fine-tune existing models for specific purposes and to use prompt engineering to command them with precision.   

The human role is not diminishing; it is evolving. As the technology becomes more accessible, the critical skills are shifting from low-level programming to high-level strategic thinking, data curation, and problem formulation. The true power of artificial intelligence is unlocked not just by the technology itself, but by the human ability to curate the right data, select the right tools from an increasingly specialized toolkit, and guide the models with expert-level command.