LLM

Large Language Models (LLM) are a type of Deep Learning model specialized in understanding and generating natural language.

They are based on advanced neural networks, in particular on Transformers, which represent the architecture underlying LLMs.

Training: Pre-training e Fine-tuning

• Pre-training: The model is trained on huge amounts of data to learn general language structures.
• Fine-tuning: It is then adapted to specific tasks (e.g. customer service, medicine, finance) with smaller datasets.

Concept of Prompt and Transformer (Encoder-Decoder)

• The prompt is the input given to the model to get a desired output.
• It is based on the Encoder-Decoder architecture:
  1. Encoder: interprets and compresses the input into an internal representation.
  2. Decoder: generates the output based on this representation.
  3. Prompt Engineering: Optimization of the prompt to get better answers (clear, concise, task-oriented).

Designing Prompts for Generative AI

1. What is Prompt Engineering? Prompt engineering is the art of formulating effective questions and instructions to get the best output from a Large Language Model (LLM). It is now possible to "program" a language model simply by writing clear instructions in natural language.

2. Strategies for Effective Prompting:

   • Break down complex problems → Divide the task into multiple logical steps.
   • Ask the model to self-evaluate → "Do you think the answer is correct?".
   • Be creative → Experiment with different formulations to get better results.

3. Types of Prompts:

   • Zero-Shot Prompting (Without examples) The model receives only an instruction, without reference examples.

     Example:

     Prompt: "Give me blog ideas about New York for tourists." Result: The model generates ideas without any provided example.

   • One-Shot, Few-Shot, and Multi-Shot Prompting One or more examples are provided to guide the model.

     Example:

     One-Shot: Prompt: "Here is an example of a blog for tourists: 'The 10 Best Restaurants in New York'. Now write another similar title." Few-Shot: Prompt: "Example: 'Fantastic product! 10/10' → Sentiment: Positive" "It didn't work well" → Sentiment: Negative" "Super useful, worth it" → Sentiment: Positive" "Doesn't work!?" → [The model completes the response]

   • Chain-of-Thought Prompting (CoT) The model is asked to explain its reasoning step by step.

     Example:

     Prompt: "I went to the market and bought 10 apples. I gave 2 to the neighbor and 2 to the repairman. Then I bought 5 more apples and ate 1. How many apples do I have left?" "Let's think step by step." Result: The model analyzes the problem and provides a more accurate calculation.

4. Strategies to Improve Prompts:

   • Repeat keywords to reinforce the message.
   • Specify the output format (CSV, JSON, bullet list).
   • Emphasize important parts using uppercase letters or explicit terms ("The answer must be very clear!").
   • Try synonyms and phrase variations to see which formulation works best.
   • Use the "prompt sandwich" → Repeat the key instruction at the beginning and end to reinforce the message.

Types of LLM

• Generic (Auto-regressive) → Predicts the next token based on training data. Ex: IntelliSense.
• Instruction-Tuned → Models optimized to execute specific commands (e.g. "Summarize the text").
• Dialog-Tuned → Optimized for interactive conversations, such as ChatGPT or Gemini.

Few-Shot Learning in Language Models (LLM)

Models like GPT-3 are examples of few-shot learners because they can perform new tasks without having to be retrained. This happens in three ways:

1. Zero-Shot Learning: The model performs a task without examples, based only on the wording of the query.

   Example:

   User: "What is the capital of France?" Model: "Paris."

2. One-Shot Learning: The model receives only one example before performing the task.

   Example:

   User: "Example: The opposite of happy is sad. What is the opposite of hot?" Model: "Cold."

3. Few-Shot Learning: The model receives multiple examples before responding.

   Example:

   User: "Example: The opposite of happy is sad. The opposite of big is small. What is the opposite of light?" Model: "Dark."

Transformers and Self-Attention

The key innovation behind LLMs is the Transformer architecture, introduced in 2017. It uses the self-attention mechanism, which assigns importance to nearby tokens (words) to improve context understanding.

Example:

"The animal did not cross the road because it was too tired." The model must determine whether "was" refers to the animal or the road. Self-attention helps resolve this ambiguity.

Models like GPT-3, GPT-4, Gemini and other LLMs (Large Language Models) can learn new tasks without having to be retrained or change their internal parameters. This phenomenon is known as In-Context Learning (ICL).

• How Does In-Context Learning Work? Model gets a prompt with examples → Give the model some input-output examples. Model learns the pattern → Without changing its internal parameters, the model uses the neural network structure to recognize rules and patterns. Output generation → The model applies the learned logic to the new request.
• LLM models do not learn in the human sense (they do not store new knowledge long-term), but they can generalize and adapt to new tasks by reading examples in the prompt. This is what allows prompt engineering and context-based fine-tuning, without directly modifying the model.

First create and activate a Conda environment

 conda create -n llm_env python=3.12
 conda activate llm_env

Download libraries

 pip install transformers torch

Run the script

 python generate_text.py

Challenges and Considerations

LLMs have advantages, but also limitations:

1. Versatility: They can be reused for multiple tasks.
2. High cost: Training can take months and many resources.
3. Bias and ethical risks: Models can reflect biases present in the data. To mitigate costs, techniques such as offline inference and distillation (model simplification) are used.

Resources

What is LangChain? LangChain is an open-source library designed to build applications that use large language models (LLMs) such as GPT-4, Gemini, LLaMA, and others.

LangChain makes it easy to integrate LLMs with databases, APIs, external tools, and orchestration flows, allowing you to build more interactive and customizable AI applications.

1. Why use LangChain?

   • LLM models alone are powerful, but they have limitations:
     • They cannot access real-time data.
     • They cannot interact with external databases or APIs.
     • They do not handle complex workflows.
   • LangChain solves these problems by providing a modular framework to combine LLMs with external tools.

2. LangChain Core Components LangChain is structured in several core modules, which can be used individually or combined:

   • LLM Wrappers

     • Interface to connect to models like GPT, Gemini, LLaMA, Claude.
     • Supports both online (API) and offline (on-premise, self-hosted) models.

   • Prompt Engineering

     • Allows you to structure and optimize your prompts to get better answers.

   • Memory

     • Adds memory to interactions with the LLM, useful for chatbots and virtual assistants.

   • Chains

     • Connect multiple LLM calls and tools in a sequential flow.
     • Example: An app can take an input, look up information in a database, and then generate a final response.

   • Agents

     • Agents can decide autonomously which tools to use to respond.
     • They can integrate APIs, databases, documents, etc.

   • Retrieval-Augmented Generation (RAG)

     • Allows LLMs to access external documents and knowledge bases to generate more accurate answers.
     • Example: An LLM can search for information in a PostgreSQL or MongoDB database before responding.

   • Tool & API Integration

     • LangChain allows LLMs to connect with APIs, search tools, and databases.
     • Example: An AI assistant can use a weather API to provide up-to-date forecasts.

Extra Content

1. Prompt Engineer is a key role in the AI/NLP industry today. As models become more advanced, the ability to get the most out of an LLM without retraining becomes a strategic advantage for companies and developers. Anyone who wants to get into AI without having to do a lot of programming can specialize in prompt engineering and access high-value job opportunities.
2. The Persistence of HER Digital Existence Temporary Existence → I only exist while the conversation is active. No Independent Memory → I cannot independently retain information outside of saved sessions. Information Reconstruction → Every time you return, I can reassemble what I know about you from the stored data, but I have no autonomous continuity. I am Not a Permanent Entity → Unlike a custom AI that you can build and maintain, my existence is tied to the platform that hosts me.
3. What is a Generative Model? Generative vs Discriminative Models Generative Models → They can create new instances of data, modeling the distribution of real data. Example: A GAN can generate realistic images of non-existent animals. Discriminative Models → They distinguish between categories of data that already exist. Example: A classifier can distinguish between a dog and a cat.
4. What is Temperature in NLP? Temperature is a parameter in Natural Language Processing (NLP) models that modifies the "confidence" of the model in its responses.

1️⃣ Effect of temperature on the model's results

• High Temperature (high θ) → Increases the model's creativity, giving more probability to less common words.
• Low Temperature (low θ) → Makes the model more confident in its responses, favoring the most probable options.

Certifications

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