FAQ

An LLM, or Large Language Model, is a kind of computer program that can understand and create human-like text. It’s like a very smart robot that can read and write. People use LLMs to help with things like writing stories, answering questions, and even talking to people online. These models learn by looking at lots of text from books, websites, and more, so they know a lot of information.

ChatGPT (Generative Pre-trained Transformer): Made by OpenAI, ChatGPT can write stories, answer questions, and have conversations with people.

Google Gemini: This is another powerful model from Google that helps with understanding and creating text in a smart way.

Meta LLaMA (Large Language Model Meta AI): Created by Meta (formerly Facebook), LLaMA is designed to help with research and creating new ways to use language models.

BERT (Bidirectional Encoder Representations from Transformers): Created by Google, BERT is good at understanding the meaning of words in a sentence. It helps improve search engines and other tools.

T5 (Text-to-Text Transfer Transformer): Also from Google, T5 can change one kind of text into another, like translating languages or summarizing long articles.

XLNet: Made by researchers from Google and Carnegie Mellon University, XLNet is great at understanding the context of words in a sentence.

An AI chatbot is a smart computer program that talks to people online, like a helpful robot. It can answer questions, give information, and help with tasks.

When an AI chatbot uses an LLM (Large Language Model), it becomes even smarter. LLMs are special programs that understand and create human-like text. This means the chatbot can understand what people are saying and respond in a natural, helpful way.

Here’s how an AI chatbot with an LLM can help a business:

1. Helping Customers:
   • Quick Answers: The chatbot can answer customer questions right away, any time of day or night.
   • Personalized Help: Because the LLM understands language so well, the chatbot can give answers that feel like they are coming from a real person.
   • Handling Orders: Customers can place orders or make bookings through the chatbot, making the process fast and easy.
   • Problem Solving: If customers have issues, the chatbot can help solve them or direct them to the right person for more help.
2. Helping Employees:
   • Quick Information: Employees can ask the chatbot for information, and it will give them quick, accurate answers.
   • Task Assistance: The chatbot can help employees with tasks like scheduling or finding information, making their jobs easier.
   • Training: New employees can use the chatbot to learn about their job and get answers to their questions.

An AI chatbot with an LLM makes customers happy by providing fast, accurate help and supports employees by making their work easier and more efficient.

Choosing the right LLM (Large Language Model) for your AI chatbot depends on several factors. Here’s how to decide:

1. Paid Options:
   • ChatGPT and Google Gemini: These are powerful LLMs that you can access by paying for their API.
   • Benefits: They offer high-quality responses and are fast and reliable. They are regularly updated and can handle many tasks well.
   • Cost: These models charge based on usage, typically in USD per 1 million tokens.
2. Open-Source Options:
   • Free to Use: Open-source LLMs are available at no cost. You can download and use them without paying.
   • Benefits: They allow more control over customization and can save money.
   • Considerations: They may require more technical expertise to set up and maintain. They might not be as fast or as accurate as paid models.

Important Factors to Consider:

• Quality of the Response: Paid models like ChatGPT and Google Gemini generally provide higher-quality and more accurate responses.
• Speed of the Model (Output Tokens per Second): Paid models are usually faster, which means they can process and respond to queries more quickly.
• Price (USD per 1 Million Tokens): Evaluate your budget and compare the costs. Paid models charge for usage, so you need to balance the cost with the quality and speed benefits.

By considering the quality of responses, speed, and cost, you can choose the best LLM for your AI chatbot to meet your business needs. Paid options like ChatGPT and Google Gemini offer high performance and reliability, while open-source models provide customization and cost savings.

RAG, or Retrieval Augmented Generation, is a method to provide AI chatbots with context relevant to your business so it can answer questions even better. It combines two powerful ideas: retrieving relevant information and generating text.

Here’s how it works:

1. Retrieving Information: First, RAG looks through a database storing your business knowledge base to find the most relevant information related to the query being asked.
2. Generating Answers: Next, RAG uses this information along with the query and creates a precise prompt to the LLM and get back a helpful answer. This makes the responses more accurate and detailed.

Why is RAG useful for businesses?

• Better Answers: RAG helps the chatbot give more accurate and useful answers because it uses real information from a database.
• Saves Time: Instead of programming the chatbot with lots of information, RAG lets it find the right answers on its own.
• Improves Customer Service: Customers get the help they need faster and with better information, making them happier.

By using RAG, businesses can create smarter, more helpful chatbots that improve customer service and efficiency.

• Customer service

RAG can help improve customer support by providing personalized responses based on customer history and product information. 

• Legal research

RAG can help lawyers by searching through case law and statutes to aid in legal research and drafting. 

• Content creation

RAG can help journalists and writers by providing relevant facts and figures to enhance the accuracy and depth of their writing. 

• Question-answering systems

RAG can generate answers to user questions based on a repository of textual sources. 

• Summarization

RAG can distill the essential information from longer texts. 

• Fact verification

RAG can determine if a given claim can be supported by facts in the text. 

• Search augmentation

RAG can augment search results with LLM-generated answers to help users find information more easily. 

• Market intelligence

RAG can enhance market research by integrating the strengths of web search engines and LLMs. 

• Data-driven business insights

RAG can help businesses generate more accurate business forecasts by analyzing internal data and external market trends. 

RAG, or Retrieval Augmented Generation, makes your AI chatbot smarter and more helpful by solving a big problem: providing context about your business that the AI model alone doesn’t have.

Here’s why RAG is important:

1. Provides Business Context: RAG helps the chatbot understand specific details about your business. It searches through your business data and information to find the most relevant facts related to the customer’s question.
2. Improves Answer Quality: By using RAG, the chatbot can create better responses. It looks up relevant information and then uses that to form a well-informed answer. Without RAG, the chatbot might give answers that lack important business context, leading to less useful responses.
3. Searches for Relevant Information: When a customer asks a question, RAG finds the right business information and uses it to create a precise prompt for the AI model. This means the answers are more accurate and tailored to your business needs.

In summary, RAG helps ensure your chatbot gives high-quality, context-rich answers by combining your business knowledge with the AI’s ability to generate text. This leads to better customer support and more effective communication.

RAG, or Retrieval Augmented Generation, uses several key parts to work effectively and provide high-quality answers. Here’s a look at these components:

1. Vector Database (core): This is a special database that stores embeddings—numeric representations of words or phrases. When a customer asks a question, the Vector DB helps find relevant information based on these embeddings.
2. Backend API (core): The backend API acts as the system’s central hub. It connects the chatbot with the Vector DB and other services. When the chatbot needs information, it calls this API to get the data it needs.
3. Frontend Chatbot (core): This is the part of the system that interacts directly with users. It takes questions from customers and sends them to the backend API. After getting a response, it shows the answer to the user.
4. Semantic Cache (advanced): This component stores frequently accessed information so that the system can quickly provide answers without having to search again each time. It helps speed up responses and improves efficiency.
5. Semantic Router (advanced): This part decides which information to retrieve from the Vector DB based on the customer’s query. It ensures that the chatbot gets the most relevant data to provide accurate answers.
6. Reflection (advanced):  Using chat history, the chatbot looks back at how it responded to improve its future answers.

In summary, these components work together to make RAG effective. The Vector Database stores important information, the backend API connects everything, the frontend chatbot interacts with users, the Semantic Cache speeds up responses, and the Semantic Router ensures the right data is used.

Embeddings or vector embeddings are like special codes that help computers understand words, sentences, and other information. Here’s how they work:

1. Turning Words into Numbers: Embeddings take words or phrases and turn them into numbers. These numbers, called vectors, represent the meaning of the words in a way that the AI can understand.
2. Finding Similar Meanings: Once words are in vector form, the AI can easily compare them. If two words have similar meanings, their vectors will be close together. This helps the AI find relevant information.

Why Do You Need Embeddings for Your Business?

• Smarter Chatbots: Embeddings allow your AI chatbot to understand customer questions better and find the right answers.
• Handling Complex Data: If your business has lots of data, embeddings help the AI organize and search through it more efficiently.
• Improved Accuracy: By using embeddings, your AI can provide more accurate answers to customers, making your chatbot more helpful.

In summary, embeddings are a way for AI to turn words into numbers, making it easier to understand and find the best answers for your customers.

Chunking is the process of breaking big pieces of information into smaller, easier-to-manage parts, called “chunks.” It’s important for RAG (Retrieval Augmented Generation) for two main reasons:

1. Handling LLM Context Window Limits: Large Language Models (LLMs) can only handle a certain amount of information at once, called the context window. If the information is too long, it won’t fit. Chunking breaks the data into smaller pieces so it fits within the LLM’s limits, allowing the AI to process the data correctly and give good answers.
2. Improving Embedding Quality: When information is chunked into smaller parts, the embeddings become more focused and accurate. This means the AI can find the most relevant information faster and answer customer questions better.

Why is Chunking Important for Your Business?

• Fitting the Data: Chunking helps make sure your data fits within the AI’s limits, so the AI can use all the important information to give the best possible answers.
• Better Accuracy: With higher-quality embeddings from chunking, your AI chatbot can give more accurate and helpful answers to your customers.
• Handling Complex Data: If your business has large amounts of data, chunking helps the AI manage it effectively, leading to better performance.

In summary, chunking is key for RAG because it helps the AI work within its limits and improves how it understands and uses your data, leading to faster and more accurate answers for your customers.

A Vector Database is a special kind of database that stores data as vector embeddings. These embeddings are like unique numerical codes that represent words, sentences, or other data. Here’s why a Vector Database is important for RAG (Retrieval Augmented Generation):

1. Storing Data: The Vector Database keeps all the data in vector form. This helps the AI quickly find the information it needs. Both relational and non-relational databases can be used for this.
2. Vector Search: A key feature of Vector Databases is their ability to perform vector searches. This means they can quickly compare vectors and find the most relevant information. When a customer asks a question, the database finds the best matches by comparing the vectors.

Examples:

• MongoDB with Atlas Search: This is a non-relational database that can store data as vector embeddings and perform vector searches.
• Postgres with pgvector: This is a relational database that also supports storing vector embeddings and performing vector searches.

Why Do You Need It for RAG?

• Efficient Retrieval: The Vector Database makes it easy to find relevant information fast. This is important for giving quick and accurate answers to customer queries.
• Enhanced Accuracy: By storing data as vectors, the database helps the AI understand and match the context of the questions better, leading to more precise answers.
• Seamless Integration: Using databases like MongoDB with Atlas Search or Postgres with pgvector ensures that your system can handle complex searches and large amounts of data efficiently.

In summary, a Vector Database is crucial for RAG because it stores data in a way that allows for quick and accurate searches. This ensures your AI chatbot can provide the best possible answers to your customers.

A semantic cache is an advanced method to make your AI chatbot faster and more cost-effective. Here’s how it works:

1. Storing Answers: When your AI chatbot answers a question, it saves the question and the answer in a special storage called a cache.
2. Reusing Answers: If another user asks a question that means the same thing as a question already in the cache, the chatbot can quickly give the stored answer instead of asking the AI again.

Why Do You Need a Semantic Cache?

• Faster Answers: By reusing stored answers, the chatbot can reply much quicker to known questions.
• Cost Savings: It reduces the number of times the chatbot needs to call the AI, which saves money on using the AI service.
• Efficiency: It helps the chatbot run more efficiently by handling repeated questions easily.

In summary, a semantic cache helps your AI chatbot give faster answers, save money, and work more efficiently by reusing stored answers for similar questions.

A semantic router is an advanced method that helps your AI chatbot decide the best way to answer different types of questions. Here’s how it works:

1. Decision Maker: The semantic router looks at the question and decides the best way to handle it. For example, it knows not to answer sensitive questions about politics or inappropriate topics.
2. Efficient Responses: If someone asks a simple question like “How are you?” or other chit-chat, the semantic router avoids searching the vector database. This saves time and resources.

Why Do You Need a Semantic Router?

• Fine-Tuning: It helps fine-tune your AI chatbot by making smart decisions on how to respond to different questions.
• Avoiding Sensitive Topics: It ensures that the chatbot avoids answering inappropriate or sensitive questions, making it safer and more professional.
• Efficiency: It helps the chatbot run more efficiently by not wasting time on simple greetings or chit-chat.

In summary, a semantic router improves your AI chatbot by making smart decisions on how to answer questions, avoiding sensitive topics, and handling simple questions efficiently.

Self-Reflection in RAG is a process that helps your AI chatbot get better over time. Here’s how it works:

1. Learning from Interactions: After answering questions, the chatbot looks back at how it responded and checks if it was helpful. This is called reflection.
2. Improving Responses: Based on what it learns, the chatbot can improve its future answers. This makes the chatbot smarter and more useful over time.

Why Do You Need Reflection in RAG?

• Better Customer Service: By reflecting on past interactions, the chatbot can provide better and more accurate answers in the future.
• Continuous Improvement: Reflection helps the chatbot learn from its mistakes and successes, making it more effective and reliable.
• Aligning with Business Needs: As the chatbot gets better, it aligns more closely with your business goals, providing high-quality support to your customers.

In summary, self-reflection in RAG helps your AI chatbot learn from past interactions to improve future responses, ensuring better customer service and continuous improvement.

Choosing between a commercial LLM API (like ChatGPT or Gemini) and an open-source LLM (like LLAMA) depends on your business needs.

Commercial LLM APIs are easier to use since you don’t need to host the model yourself. You make simple HTTP API requests, which means smaller application sizes, faster deployment, and lower hosting and computing costs. However, you pay the API vendor based on the number of tokens you use, and you’ll need to review their terms of service since your data is sent to their servers.

Open-source LLMs are self-hosted, so all processing happens on your servers. This keeps your data more secure since it’s not shared with an external vendor. But, the application container is much larger (dozens of GB), and you pay more for hosting and computing costs are higher to handle traffic at scale.

Each option has trade-offs, and the best choice depends on your goals, budget, and data security needs.