ad
ad

The Easy Way To Improve Your AI Knowledge Base [Voice AI Agents]

People & Blogs

Introduction

When developing voice AI agents, one of the initial challenges you will encounter is creating an effective knowledge-based system. Many developers, including myself, have faced significant hurdles in this regard. Despite the improvements I have made over time, I continue to seek ways to enhance these systems. In this article, I will share the critical lessons I've learned about improving knowledge-based systems, helping you avoid the pitfalls I encountered.

Understanding Knowledge-Based Systems

Before diving into the improvements, let’s define what constitutes a good knowledge-based system. At its core, an effective knowledge base should:

  1. Retrieve Relevant Context: It needs to provide information that exists outside the LLM (Large Language Model) training data. LLMs often do not have knowledge of specific client data or employee information, leading to potential "hallucinations" or incorrect answers when a user asks questions that require domain-specific knowledge.

  2. Digestible Context: The information retrieved must be presented in a way that's easily understandable by the AI agent. The presentation of this information is essential to ensure a positive user experience. For instance, in an inbound receptionist scenario I developed, the agent's responses sometimes became lengthy, taking 30 to 40 seconds to answer a question. By refining the context further, I was able to create more compact and relevant responses.

Platform Configuration for Effectiveness

I utilize a platform called Vector Shift to build my knowledge retrieval and generation (RAG) pipelines. Here’s a basic outline of how I configure my knowledge-based systems on this platform:

  1. Knowledge Base Configuration:

    • Naming and Chunk Size: When creating a new knowledge base, find a suitable name and set a chunk size—around 1024 characters often serves as a good balance between detailed data and usability.
    • Chunk Overlap: Establish overlap between chunks (e.g., 256 characters). This ensures that crucial information isn't missed while still keeping the context manageable.

  2. Using Effective Models:

    • Processing Model: I use the "Llama Pass" for document processing as it efficiently handles various file types.
    • Embedding Model: For embeddings, I prefer "Text Embedding three large" from OpenAI due to its performance.

  3. Building Pipelines:

    • Create input pipelines that start with user questions. An LLM like Google’s is used to refine the inquiry into a better query format.
    • This refined query is sent to the knowledge reader, which retrieves relevant context.
    • Another LLM processes this context, providing concise, step-by-step recommendations for the voice AI agent. This stage is crucial as it keeps answers clear, concise, and easily actionable.

Implementation and Integration

Once the pipeline is built, I can integrate it into my voice AI tool seamlessly. By utilizing scenarios in Make.com, I set up requests to run my Vector Shift API. The process involves sending user questions to the API and receiving structured replies that aid the voice AI agent in generating responses.

Conclusion

The insights shared in this article represent my approach to enhancing AI knowledge bases for voice agents. By understanding the components of an effective knowledge-based system, carefully configuring models and pipelines, and ensuring digestibility of responses, you can significantly improve the performance of voice AI agents.

Keyword

  • voice AI
  • knowledge-based systems
  • RAG pipelines
  • LLM
  • context retrieval
  • digestible responses
  • Vector Shift
  • Llama Pass
  • OpenAI embedding

FAQ

1. What is a knowledge-based system in voice AI?
A knowledge-based system retrieves and supplies relevant information that helps a voice AI agent answer user inquiries more accurately.

2. Why is chunk size important in a knowledge base?
The chunk size balances detail and clarity, as overly detailed responses can overwhelm the AI, while excessively simplified ones can lead to missing crucial information.

3. What role does context digestibility play in voice AI systems?
Digestibility ensures that responses are clear, concise, and user-friendly, improving the overall experience for individuals interacting with the voice AI agent.

4. How can I improve the effectiveness of my voice AI agent?
Focus on retrieving specific, relevant context using a well-structured knowledge base and presenting it in a digestible format for the AI to utilize in its responses.