How to Build an AI Assistant: An In-Depth Guide

May 1, 2023
33 min read

AI-powered assistants have become an essential tool for businesses, helping to streamline communication, automate tasks, and enhance user experiences. By integrating assistants with widely-used platforms and their products, companies can achieve improved efficiency, collaboration, and customer satisfaction.

In this article, we'll explore various use cases of AI assistants and provide valuable insights and practical guidance on how to integrate an AI into existing platforms to harness their full potential.

Use Cases for AI Assistants

  • Company Executives: For busy executives in large organizations, AI assistants can be lifesavers. They help keep track of decisions made, project updates, team tasks, and any challenges faced by team members. By analyzing internal company information, chatbots can provide executives with critical insights for better decision-making.
  • Company Employees: AI assistants can lend a helping hand to employees in their day-to-day work, from content creation and user interface design to information analysis and CRM management. Assistants can streamline processes and increase productivity.
  • Customer Self-Service: AI chatbots can empower customers to perform actions independently, such as finding products, making purchases, and tracking orders. Chatbots enable seamless self-service experiences for customers.
  • Customer Support: Providing personalized customer support is easier than ever with AI chatbots. By generating natural language responses based on company and customer information, chatbots can offer support in a desired tone of voice, making interactions feel genuine and personal.
  • Personal Usage: AI assistants can simplify our daily lives by organizing and analyzing personal information. From task reminders and answering personal questions to automating routine chores, chatbots can make life more manageable and efficient.

Understanding Large Language Models (LLMs)

Large Language Models (LLMs) are the driving force behind AI chatbots, enabling them to understand and generate human-like responses. Trained on vast text datasets, LLMs use advanced algorithms to process inputs and produce context-aware outputs that mirror natural human language

Capabilities of LLMs

LLMs possess a diverse array of powerful capabilities, making them invaluable for AI assistants:

  • Contextual understanding: LLMs excel at grasping context within text, allowing chatbots to generate relevant responses.
  • Human-like text generation: LLMs can create responses that closely mimic human speech, elevating the user experience.
  • Question-answering: LLMs can provide accurate answers to user queries based on available text data.
  • Text summarization: LLMs can transform large volumes of text into concise summaries, helping users quickly grasp key points.
  • Language translation: LLMs can translate text across multiple languages, supporting cross-cultural communication.
  • Text classification and categorization: LLMs can sort and organize text based on predefined criteria.
  • Sentiment analysis: LLMs can identify the emotional tone within text, enabling chatbots to respond accordingly.
  • Rewriting text: LLMs can rephrase text while retaining the original meaning, ensuring readability and engagement

These impressive capabilities contribute to the extensive applications of AI chatbots across a variety of industries.

Limitations of LLMs

While LLMs are undeniably powerful, it's essential to be aware of their limitations to design more effective AI chatbots:

  • Real-time or dynamic data processing: LLMs can struggle with processing rapidly changing or real-time data.
  • Input phrasing sensitivity: LLMs might produce varied responses for similar queries based on input phrasing differences.
  • Plausible but incorrect answers: LLMs can occasionally generate answers that appear reasonable but are, in fact, incorrect.
  • Biases in training data: LLMs can inherit biases from their training data, which may lead to skewed or biased responses.

Understanding these limitations helps developers create better AI chatbots and address potential challenges proactively.

Popular LLMs

There are several notable LLMs available in the market, each with unique features and capabilities. Some of the most popular ones include:

  • OpenAI's GPT series: Known for their cutting-edge performance, GPT models have set new benchmarks in natural language processing.
  • Google BERT: BERT revolutionized the field of NLP with its ability to analyze text from both directions - left to right and right to left, enabling a deeper understanding of context.
  • Anthropic Claude: LLM for conversations, question answering, and workflow automation based on research into training honest and responsible AI systems It is used in Notion, Quora, and DuckDuckGo.

As each model offers different strengths, businesses can choose the one that best aligns with their specific use cases and requirements.

How to Integrate AI Assistants with Existing Platforms

Integrating AI assistants with existing platforms involves a series of steps that enables the assistant to understand context, search relevant data, and provide meaningful responses.

While AI can be integrated with any API-enabled platform, we'll demonstrate the integration process using Slack, a popular messenger, as an example. Here, we'll break down the process into a simple workflow.

1. Data Collection from Sources

The first step involves collecting data from various sources to create a context for the AI assistant. For example, in the case of Slack, the data might include messages from different channels or direct messages.

To obtain this data, the integration uses APIs from the platforms it aims to integrate with, such as the Slack API.

2. Filtering and Creating Embeddings for Relevant Data

Once the data is collected, the Text Embedding Model creates embeddings for every piece of text. Embeddings are numerical representations of text, such as words or phrases, that allow the AI to process and understand the context.

Here is a list of the most popular text embedding models:

3. Storing Data in Vector Databases

After creating the embeddings, the data is stored in a vector database. Vector databases enable efficient similarity search and retrieval, which is crucial for the AI chatbot to find relevant information when answering user queries.

Here are some popular vector databases:

4. Recognizing User Requests and Retrieving Custom Data

When the assistant receives a query, such as "What has the #ai channel been up to this week?", it searches the vector database for the top N messages that are similar to the query using the embeddings. This step ensures that the AI assistant has the most relevant information to answer the user's question.

5. Responding to User Queries

With the retrieved texts in hand, a prompt for the LLM is constructed. The prompt could look like this: "You are a helpful personal assistant. A user asked the following query: [received query here]. Use the following context to answer the query: [retrieved texts]. Return the answer only."

The prompt is then sent to the LLM, which generates a response based on the provided context. Finally, the response is displayed to the user.

6. Executing Actions

AI assistants can execute actions within the platforms they are integrated with. As these platforms support APIs, a wide range of tasks can be performed using their respective APIs. The workflow is as follows:

  1. The LLM analyzes the user's request and determines the appropriate APIs and their parameters needed for the task.
  2. Once identified, the assistant calls the necessary APIs, seamlessly completing the desired action.

Challenges and Considerations

LLM Usage Costs

Large Language Models (LLMs) require extensive training to become useful. During this process, they learn human language nuances and even domain-specific information. While major players like OpenAI, Google, and Meta offer advanced, pre-trained LLMs (which can be quite expensive), there are also open-source alternatives that can be trained using custom data or on platforms like Hugging Face.

We predict that as technology advances, the cost of LLM usage is expected to decrease over time.

LLM pricing models often vary, but they typically utilize tokens to determine costs. Tokens represent segments of text that the LLM processes. For instance, the string "ChatGPT is great!" is encoded into six tokens: ["Chat", "G", "PT", " is", " great", "!"].

Keep in mind that both input and output tokens count towards these quantities. For example, if an API call used 10 tokens in the message input and received 20 tokens in the message output, it would be billed for a total of 30 tokens.

Chaining Calls to LLMs

In certain situations, AI chatbots need to engage in more nuanced interactions with LLMs to generate accurate and contextually relevant responses. Chaining calls to LLMs enables the chatbot to have back-and-forth interactions with the LLM, refining the response based on previous exchanges.

For example, within a Slack channel, a user asks the AI assistant about the current status of a project. The assistant retrieves relevant messages but provides an incomplete summary. The user then asks for more specific details about a particular aspect of the project.

The default LLM’s functionality doesn’t support chaining calls, so the external tool should be used for this. There are several such tools; the most popular ones include LangChain, GPT-Index, and GODEL. Choose one that fits the business needs.

Customizing LLM Output

To cater to specific business requirements, AI chatbots often need to generate tailored responses that align with the organization's tone, language, or industry.

There are several methods to customize LLM output:

  • Fine-tuning: Training the LLM on a smaller, specialized dataset helps the model understand the nuances of your domain better. Fine-tuning can refine the LLM's responses to better suit your organization's context and goals.
  • Data labeling (annotation): Annotating a dataset with human-generated labels or metadata enhances the LLM's understanding of the desired output. By providing additional context, data labeling can guide the LLM towards generating more relevant responses.
  • Domain adaptation: Adapting the LLM to a specific domain, such as finance, healthcare, or legal, ensures that the model's responses are better suited to the unique requirements of that industry. Domain adaptation involves training the LLM on industry-specific datasets and fine-tuning it for the desired domain.

Addressing Data Privacy Concerns

Data privacy is a paramount consideration when incorporating AI into business workflows. The challenge lies in the fact that even if the AI assistant itself doesn't utilize user data, the underlying LLM may employ this information for self-improvement purposes, potentially compromising data privacy. It's essential to understand how the LLM manages and uses user data to guarantee adherence to privacy regulations.

OpenAI's GPT does not use customer data submitted via API to train OpenAI models or improve OpenAI's service offering. Users can also disable data collection on the ChatGPT website to prevent tracking of their data sent to ChatGPT.

AI Response Moderation

Although pre-trained LLMs usually have built-in moderation for their responses, there are instances where additional moderation is necessary. This extra layer of moderation helps maintain brand reputation, prevent offensive content, and ensure compliance with legal and ethical standards.

To enhance AI response moderation, you can also leverage AI itself. Create a list of prohibited filters and keywords, then instruct the AI to validate each response against these rules. This approach ensures a higher level of content control while maintaining the benefits of AI-generated responses.

Furthermore, several LLM providers supply tools specifically designed for message moderation, like OpenAI's moderation endpoint.

Global AI Regulations

As AI technology continues to advance, governments worldwide are taking steps to establish regulations to protect citizens, businesses, and the ethical use of AI. These regulations can vary significantly between countries and regions, making it essential for businesses implementing AI solutions to stay informed and comply with the relevant rules and guidelines.

Some key aspects of global AI regulations include:

  • Data privacy and protection: Ensuring that AI systems adhere to data protection laws like GDPR in the European Union, CCPA in California, or similar regulations in other regions is crucial. These laws require businesses to protect user data, provide transparency in data usage, and enable users to control their personal information.
  • Ethics and bias: Governments and organizations are working to develop guidelines and best practices to minimize biases in AI systems. These guidelines aim to promote fairness, accountability, and transparency in AI decision-making processes, ensuring that AI systems treat users without discrimination.
  • Accountability and liability: AI regulations may establish rules for assigning responsibility for AI system decisions and actions, which could impact businesses using AI technologies. Understanding who is responsible when an AI system causes harm or makes a mistake is crucial for businesses to navigate potential legal and ethical issues.
  • Copyright and intellectual property: As AI systems generate content and develop ideas, questions arise about ownership and copyright. Regulations may evolve to address these issues and clarify the legal rights associated with AI-generated content. Businesses employing AI systems should monitor these developments to ensure compliance and protect their intellectual property.


In conclusion, AI assistants, powered by advanced Large Language Models (LLMs), are transforming the way businesses communicate, collaborate, and operate. By integrating AI into existing platforms, organizations can leverage personalized, context-aware assistance to address various challenges and optimize their workflows. As LLMs continue to evolve and improve, AI chatbots will play an increasingly crucial role in redefining the future of human-computer interaction. By staying informed and adaptive, businesses can harness the potential of AI-driven innovations, paving the way for success and growth in the digital era.

Elijah Atamas

CEO, Softcery
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