What is LLM (Large Language Model)?

Understanding the Foundations of Modern Artificial Intelligence

Large language models (LLM) are the fundamental building blocks of most modern generative artificial intelligence applications. These are exceptionally complex neural networks trained on enormous quantities of textual data, capable of understanding, summarizing, translating, predicting, and generating human language with astounding accuracy. Their ability to mimic human communication has opened doors to numerous new applications, from advanced chatbots to tools for automated content creation.

How Do Large Language Models Work?

The core of most modern LLMs is the Transformer architecture, which was first introduced in 2017. This architecture revolutionized the field by introducing the self-attention mechanism, which enables the model to weigh the importance of different words in the input sequence while processing text. Rather than processing words sequentially, as older models did (e.g., RNN), the Transformer can analyze entire text at once and thus better understands context and relationships between words. Simply put, LLM models are used by ChatGPT, Claude, and others.

Due to their versatility, LLMs have found their way into numerous business applications:

• Customer Support: creating intelligent chatbots that can answer complex customer questions 24/7.
• Marketing and Sales: automated writing of marketing copy, emails, social media posts, and even video content scripts.
• Software Development: assisting programmers in writing, debugging, and optimizing code.
• Data Analysis: summarizing long reports, analyzing customer opinions, and identifying trends in unstructured data.

Despite their remarkable capabilities, LLMs also have important limitations that must be understood before their implementation:

• Hallucinations: because models operate on the basis of probability, they can sometimes generate information that appears convincing but is actually false or completely fabricated. This happens because the model has no true understanding of reality, but merely assembles words into statistically probable sequences.
• Knowledge Obsolescence: an LLM's knowledge is limited to the data it was trained on. The model has no access to real-time information, so its answers can be outdated. Updating the model with new data is an expensive and time-consuming process.
• Limited Context: each model has a limited "context window" size – this is the amount of text it can consider at once. With very long documents or conversations, the model can lose track and forget information mentioned at the beginning.
• Bias: LLMs learn from enormous quantities of internet text, which also reflect human prejudices and stereotypes. These biases can inadvertently be transferred into the model's responses.

What is RAG (Retrieval-Augmented Generation)?

As we have seen, large language models, despite their power, have obvious shortcomings, particularly in the area of access to specific, current, and reliable information. This is where Retrieval-Augmented Generation (RAG) technology comes into play, representing one of the most important advances in the practical application of LLMs. RAG is not a new model, but rather an architectural approach that combines the power of generative models with the reliability of information retrieval systems.

Detailed Explanation of the Concept

The basic idea of RAG is simple: before the LLM generates an answer, we provide it with relevant information from an external, verified data source. Instead of the model relying solely on its internal, static knowledge, we enable it to access a specific knowledge base – for example, internal company documentation, technical manuals, legal documents, or a product database. In this way, we "ground" the LLM in facts and prevent it from fabricating answers.

Why Does RAG Solve Key LLM Problems?

• Reduces Hallucinations: because the model builds its answer based on retrieved documents, the probability that it will fabricate facts is significantly lower.
• Ensures Currency: the knowledge base can be continuously updated with new information without needing to retrain the entire LLM. This allows the system to always have access to the latest data.
• Enables Verifiability: answers generated by a RAG system can be accompanied by sources from which the information was obtained. This enables users to verify the accuracy and source of information, which increases confidence in the system.
• Increases Relevance: the system can answer questions about specific, internal company topics that general LLMs have no knowledge of.

Examples of Business Use

• Advanced Internal Knowledge Base: employees can ask in natural language about internal procedures, policies, and technical documentation.
• Intelligent Customer Support: chatbots that do not just provide general answers, but answer specific questions about products, orders, and solve problems based on a knowledge base.
• Automated Contract Analysis: a system that helps lawyers quickly find relevant clauses and risks in extensive legal documents.

What Are AI Workflows?

While LLM and RAG focus on language processing and generation, AI Workflow (AI workflow) represents a step forward in automating entire business processes. It is no longer just about one answer to one question, but rather about orchestrating multiple sequential steps that can include different AI tools, systems, and human approvals.

Definition of AI Workflow

An AI Workflow is a predetermined sequence of tasks that are executed automatically to achieve a specific business objective. The key characteristic of a workflow is its deterministic nature. This means that the steps, rules, and decision points (e.g., if the invoice is less than €1,000, send it for approval to the manager) are predefined. The course of the process is predictable and repeatable. AI in these workflows acts as an exceptionally powerful tool for executing individual tasks, such as document classification, data extraction, or text summarization.

Advantages:

• Reliability and Predictability: because the rules are fixed, the results are consistent and reliable.
• Efficiency: automating routine, repetitive tasks saves enormous amounts of time and reduces the possibility of human error.
• Traceability: every step in the process is recorded, enabling easy monitoring and auditing.

Limitations:

• Rigidity: workflows are difficult to adapt to unexpected situations or exceptions that were not anticipated.
• Setup Complexity: planning and implementing complex workflows with many decision points can be demanding and time-consuming.
• Limited to Defined Processes: workflows are suitable only for processes that can be fully described with predetermined rules.

What Is an AI Agent?

If AI workflows are comparable to an exceptionally efficient worker on an assembly line who precisely follows instructions, then AI agents are more like an experienced project manager who receives a goal and then independently plans the path, chooses tools, and makes decisions to achieve that goal. An AI agent represents a shift from automating individual tasks to automating entire roles and responsibilities.

Definition of AI Agent

An AI agent is an autonomous system that can perceive its environment, make decisions, and take actions to achieve a specific goal. Unlike a workflow, the path of an agent is not predetermined. An agent has the ability to think, plan, and adapt its strategy based on current information and the results of its actions. Its operation is not deterministic, but rather non-deterministic – to achieve the same goal, it can take different paths in different situations.

The key characteristics that distinguish agents from simpler AI systems are:

• Autonomy: an agent can operate for extended periods without direct human supervision.
• Memory: an agent can remember past interactions, results, and mistakes and learn from them to improve its future performance.
• Decision-Making and Planning: the central capability of an agent is to divide a complex problem into smaller steps based on the goal and current state, create a plan, and adapt it as needed.
• Tool Use: agents are not limited to merely generating text. They can be equipped with various tools, such as internet access for searching information, the ability to write and execute code, access to internal company APIs, or the ability to use other software applications.

The Difference Between AI Workflow and AI Agent: Key to Making the Right Choice

Understanding the fundamental difference between AI workflows and AI agents is key to choosing the right technology for solving a specific business challenge. While both approaches deal with automation, they do so in fundamentally different ways. Choosing the wrong approach can lead to inefficient, unreliable, or overly complex solutions.

The most concise way to summarize the difference is as follows:

• AI Workflow automates tasks. It follows predetermined rules and steps. It is like a recipe – if you follow it precisely, the result will always be the same. Its strength is in repeatability and reliability.
• AI Agent automates goals. It receives a final goal and independently plans the path to it. It is like an experienced chef who you tell to prepare dinner for four people, and he will check the ingredients in the refrigerator, decide on the dishes, and prepare them. Its strength is in autonomy and adaptability.

When to Use Workflow and When to Use an Agent?

Use AI Workflow when:

• The process is well-defined, standardized, and rarely changes.
• The highest priority is reliability, repeatability, and 100% predictability.
• It involves routine administrative tasks, such as data entry, generating standard reports, or moving data between systems.
• Errors are unacceptable and every exception requires human intervention.

Use AI Agent when:

• The goal is clear, but the path to the goal is not unambiguous and requires exploration and adaptation.
• The process involves interaction with multiple different systems, data sources, or tools.
• A certain degree of autonomy and "thinking" is needed to overcome obstacles.
• It involves tasks that are typically performed by analysts, researchers, or assistants: gathering information, preparing complex summaries, identifying opportunities.

The Role of MMStudio - From Theory to Practical Implementation

At MMStudio, we recognize that the path from understanding concepts to successfully implementing advanced AI systems is full of challenges. Our approach is not based on selling solutions, but rather on deep professional understanding of the technology and its practical limitations. Over the years, we have gained valuable insight into what works in practice and what does not.

Technologies such as LLM, RAG, AI workflows, and AI agents are not just a current trend, but rather represent the foundations of the next generation of business software. The difference between a workflow and an agent – between automating tasks and automating goals – will become increasingly important. Companies that will know how to properly leverage the power of both approaches will have a key competitive advantage.

The future is not in choosing between workflows and agents, but in their synergy. Imagine complex hybrid systems where autonomous agents oversee and manage a multitude of reliable workflows, while humans retain the role of strategic director and supervisor.

The world of artificial intelligence is evolving at lightning speed, but with a solid understanding of the fundamentals, you will be prepared for whatever the future brings.