IBL News | New York
To create a corporate chatbot for customer support, generate personalized posts and marketing materials, or develop a tailored automation application, the Large Language Model (LLM), like GPT-4 has to include the ability to answer questions about private data.
However, training or retraining the model is impractical due to the cost, time, and privacy concerns associated with mixing datasets, as well as the potential security risks.
Usually, the approach taken is “content injection,” a technique called “embedding” that involves providing the model with additional information from a desired database of knowledge alongside the user’s query.
This data collection can include product information, internal documents, or information scraped from the web, customer interactions, and industry-specific knowledge.
At this stage, it’s essential to consider data privacy and security, ensuring that sensitive information is handled appropriately and in compliance with relevant information, as expert Shelly Palmer details in a post.
The data to be embedded has to be cleaned and structured to ensure compatibility with the AI model.
Also, it has to be tokenized and converted into a suitable format by setting the correct indexes.
After data is preprocessed, the AI model has to be fine-tuned and pre-trained.
The next step is to interact with the API. Query vectors will be matched to the database, pulling the content that will be injected.
The number of tokens is calculated to know the cost. Usually, each token corresponds to four or five English-language words.
To run an effective content injection schema, a prompt must be engineered. This is an example of a prompt:
“You are an upbeat, positive employee of Our Company. Read the following sections of our knowledge base and answer the question using only the information provided here. If you do not have enough information to answer the question from the knowledge base below, please respond to the user with ‘Apologies. I am unable to provide assistance.’
Context Injection goes here.
Questions or input from the user go here.”
There are three more considerations for the right implementation: Any personally identifiable information (PII) must be anonymized in order to protect the privacy of your customers and also ensure compliance with data protection regulations like GDPR (General Data Protection Regulation).
Robust access control measures will help prevent unauthorized access and reduce the risk of data breaches.
Continuous monitoring is in place in order to check for any signs of bias or other unintended consequences before they escalate.
• Blog Replit: How to train your own Large Language Models
• Andreessen Horowitz: Navigating the High Cost of AI Compute
