In the ever-evolving business landscape, companies are increasingly turning to artificial intelligence (AI) to gain a competitive advantage, optimize operations, and unlock new possibilities.
Artificial intelligence, the simulation of human intelligence in machines, has seen unprecedented growth in recent years. Its ability to analyze large data sets, identify patterns, and make intelligent decisions has made it a game-changer in various industries. Companies are increasingly integrating AI into their operations to improve efficiency, drive innovation and gain a competitive advantage.
The integration of AI into various facets of business has marked a paradigm shift, revolutionizing the way companies operate, make decisions and interact with customers. In this article we explore the various applications of AI in businesses and delve into strategies for successful implementation.
Components or pieces to take into account to implement Artificial Intelligence in your organization
Implementing artificial intelligence (AI) in an organization involves careful planning, consideration of various factors, and a strategic approach. There are tools, such as the well-known ChatGPT, that can be adapted to the specific needs of the company and be very useful to apply in daily work processes.
To ensure that ChatGPT is aware of the company's workflows, in addition to its information and data, several steps must be followed, the first being:
Prepare data for modeling (vectorizing)
Preparing data for modeling, often called data preprocessing, is a crucial step in building an AI model. The specific techniques you use depend on the type of data you have and the nature of your AI task. The first thing to do is collect relevant data for each task that you want to request from ChatGPT. Make sure your data set is representative of the problem you are trying to solve.
Then, you must move on to vectorization. Convert text or categorical data into numerical vectors that can be used as input to machine learning models. Techniques include Bag-of-Words, TF-IDF (Term Frequency-Inverse Document Frequency), word embeddings (e.g., Word2Vec, GloVe), etc.
Data processing to make it ready for artificial intelligence
The data, after it has been collected and vectorized correctly to be entered into ChatGPT, or whatever artificial intelligence tool you decide to use, must be processed and go through several stages so that natural language can understand it. Must be:
- Convert categorical variables: If your data includes categorical variables, encode them in numeric format. Common techniques include one-hot encoding or tag encoding.
- Standardize or normalize numerical features: Scale numerical features to a standard range to avoid bias.
- Handle outliers: Decide how to handle outliers, either by removing them or transforming them.
The text, divide it into individual words or sub words. Eliminate common words that don't add much to the meaning. Reduce words to their base or root form.
Use of prompt to ask questions and requests to artificial intelligence
When interacting with artificial intelligence models like ChatGPT, using prompts is a common approach to asking questions or requests. A prompt is a command or input given to the AI system to generate a response.
As the main advice, you must be clear and specific in your instructions. The more precise your input is, the better the AI response will be. Instead of vague queries, provide detailed questions or instructions to get the information or assistance you need.
A company that has a ChatGPT already trained with all the company's information, data and documentation, can use this advantage to strengthen cybersecurity, maintain control over its data and ensure that it does not leave its facilities.
Industries with strict regulatory requirements may find AI tailored to their needs more suitable. Keeping AI systems on-site allows organizations to comply with data protection regulations and industry standards without relying on external cloud services.
Langchain: the framework to work on business artificial intelligence
What is Langchain?
As they explain in their official documentation “LangChain is a framework for developing applications based on language models. It enables applications that are context-aware, connecting a language model with sources of context (quick instructions, some examples, content on which to base your response, etc.).”
In short, LangChain is an open source framework that allows software developers working with artificial intelligence (AI) and its subset of machine learning to combine large language models with other external components to develop LLM-based applications. LangChain's goal is to link powerful LLMs, such as OpenAI's GPT-3.5 and GPT-4, to a variety of external data sources to create and leverage the benefits of natural language processing (NLP) applications.
How to use Langchain to work with local artificial intelligence
As we had explained before, Langchain is specifically designed to create applications that are supported by language models, ideal for integrating it into a business project driven by artificial intelligence, such as ChatGPT.
Langchain allows for a sophisticated level of integration, allowing the application to connect with other data sources and also interact with the entire environment. The framework can connect a language model to other data sources, allowing you to incorporate external data into your decision-making process.
To use LangChain, you must first choose an LLM provider. There are several different LLM providers available, including OpenAI, Cohere, and Hugging Face. After choosing an LLM provider, you can create a LangChain application by creating a chain, which is a sequence of LLMs that work together to accomplish a task. For example, you could create a chain that first uses an LLM to generate a draft of a text. You could then use another LLM to edit the text and improve its quality.
To upload local information to Langchain, for example a PDF, follow these steps:
from langchain.document_loaders import PyPDFLoader
loader = PyPDFLoader("docs/your_pdf_name.pdf")
pages = loader.load()
Each page is a Document. A document contents text (page_content) and metadata.
len(pages) page = pages[0] print(page.page_content[0:500]) page.metadata
Langchain for prompt creation: templates to give instructions to artificial intelligence
In the official Langchain documentation they explain “A prompt for a language model is a set of instructions or inputs provided by a user to guide the model's response, helping it understand the context and generate relevant and consistent results based on the language, such as answer questions, complete sentences or participate in a conversation.”
Langchain offers developers the option of templates to give direct instructions to ChatGPT or any tool used for local artificial intelligence. They explain their creation this way:
from langchain.prompts import ChatPromptTemplate
chat_template = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful AI bot. Your name is {name}."),
("human", "Hello, how are you doing?"),
("ai", "I'm doing well, thanks!"),
("human", "{user_input}"),
]
)
messages = chat_template.format_messages(name="Bob", user_input="What is your name?")
How to connect local artificial intelligence with the world
Connecting local artificial intelligence (AI) systems to the world involves integrating your AI applications or models with external services, networks or platforms. Langchain is a technology that allows you to achieve this.
Expose your AI functionality through well-documented APIs. APIs allow external systems or applications, such as Langchain, to communicate with your AI system. Ensure your API endpoints are secure and follow industry standards.
Final words
Having a local artificial intelligence system, such as in this case a ChatGPT where we have recorded all the company's data, allows us to compare this with data from around the world using a link system, in order to enhance internal processes, improve them and adapt them to new trends. We suggest:
- Define objectives and key performance indicators (KPIs): Clearly define the objectives you want to achieve by comparing data. These objectives could be related to improving efficiency, reducing costs, improving accuracy, or achieving other specific goals. Identify key performance indicators (KPIs) that will help measure success.
- Data Collection and Integration: Collect relevant data from your local AI systems. This may include process-related data, performance metrics, user interactions, or any other relevant information. Integrate data from different sources to create a complete data set.
- Data Cleaning and Preprocessing: Clean and preprocess data to ensure accuracy and consistency. Address missing or erroneous values and format the data so that it is suitable for analysis. This step is crucial to obtain reliable information.
- Data analysis and comparison: Use statistical and analytical techniques to compare data. This could involve comparing performance metrics, identifying trends, or evaluating the impact of different variables on workflows. Data visualization tools can be helpful in presenting findings.
At Rootstack, we have the ideal team of experts to help you implement an artificial intelligence system adapted to the specific needs of your company, contact us.
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