Common Mistakes in the Development of AI Assistants

How AI assistants are often developed for companies, why they fail and what we can learn from them.

“The fault-finder will find faults even in paradise in the development of AI assistants.”
(Henry David Thoreau ChatGPT)

How fortunate that people make mistakes: because we can learn from them and improve. We have closely observed how companies around the world have implemented AI assistants in recent months and have, unfortunately, often seen them fail. We would like to share with you how these failures occurred and what can be learned from them for future projects: So that AI assistants can be implemented more successfully in the future!

How AI assistants have been developed in companies so far

A Story in 5 acts: Pitfalls in AI assistant development

Chapter 1: The vision

Every project involving AI assistants starts with an ambitious vision: to develop an assistant that can master specific challenges - from answering complex questions to analyzing business data.

The goals are clear: to increase process efficiency, speed up decision-making and ultimately drive the company's success.

Chapter 2: The research

After thorough research, development teams choose advanced AI tools and platforms, convinced by their maturity and the many success stories in the market.

Popular options such as LangChain, LlamaIndex and QDrant provide the necessary technology to realize ambitious AI assistants.

The development phase is characterized by innovation and creativity. Teams immerse themselves in the world of vector databases, develop intelligent agents and experiment with complex algorithms to bring the first prototypes to life.

The initial results are promising: the AI assistants provide precise and well-founded answers, a strong indication of the project's potential.

Chapter 3: The problems become apparent

However, unexpected difficulties arise after the successful launch.

The systems are prone to errors and "hallucinations", especially for specific queries, which undermines confidence in the technology.

The realization that even advanced AI systems have their limitations leads to the search for solutions to improve accuracy and reliability.

Chapter 4: In search of a solution

Fortunately, public resources offer many ways to improve the accuracy of a misbehaving AI assistant. Well, at least that's what they claim... Common ideas include:

  • Better prompting

  • Tweaking retrieval and fine-tuning embeddings

  • Query transformations (expansion)

  • Chunk reranking

  • Adding multiple agents building routers

What actually results from this? Often an unnecessarily complex architecture of the AI assistant. Despite attempts to improve it, it will not always be able to answer simple questions. It will lack accuracy and occasionally generate incorrect information: The system will still hallucinate.

What were the pitfalls that caused the team to fail?

At this point, we have good news: The mistakes that many teams make when developing AI assistants, as described here, can be traced back to three frequently incorrect assumptions:

3 common, incorrect assumptions in AI assistant development

False Assumption 1

"Publicly available materials and articles tell the full truth about how cutting-edge AI systems actually work in business."

False Assumption 2

"If we break our documents into small pieces and then insert them into a vector database, the AI will be able to create meaning from them as if by magic."

False Assumption 3

"We need to build an all-powerful AI assistant with complex architecture to bring real value to the business."

What concrete measures can be derived to successfully implement AI assistants?

Discover our tried-and-tested tips & tricks to help you avoid common pitfalls during implementation and build good AI assistants for companies cost-effectively.

8 tips for developing AI assistants

Would you like to use KI Assistants in your company?

Then we look forward to hearing from you.

* required

We use the data you send us only for contacting you in connection with your request. You can find all further information in our privacy policy.

Please solve captcha!

captcha image
Martin Warnung
Sales Consultant TIMETOACT GROUP Österreich GmbH +43 664 881 788 80
Contact
Blog 7/22/24

So You are Building an AI Assistant?

So you are building an AI assistant for the business? This is a popular topic in the companies these days. Everybody seems to be doing that. While running AI Research in the last months, I have discovered that many companies in the USA and Europe are building some sort of AI assistant these days, mostly around enterprise workflow automation and knowledge bases. There are common patterns in how such projects work most of the time. So let me tell you a story...
Blog 5/17/24

8 tips for developing AI assistants

8 practical tips for implementing AI assistants
Headerbild zu IBM Watson Assistant
Technologie

IBM Watson Assistant

Watson Assistant identifies intention in requests that can be received via multiple channels. Watson Assistant is trained based on real-live requests and can understand the context and intent of the query based on the acting AI. Extensive search queries are routed to Watson Discovery and seamlessly embedded into the search result.
Headerbild zu IBM Watson Assistant
Technologie

IBM Watson Assistant

Watson Assistant identifies intention in requests that can be received via multiple channels. Watson Assistant is trained based on real-live requests and can understand the context and intent of the query based on the acting AI. Extensive search queries are routed to Watson Discovery and seamlessly embedded into the search result.
Headerbild zu IBM Watson Assistant
Technologie

IBM Watson Assistant

Watson Assistant identifies intention in requests that can be received via multiple channels. Watson Assistant is trained based on real-live requests and can understand the context and intent of the query based on the acting AI. Extensive search queries are routed to Watson Discovery and seamlessly embedded into the search result.
Headerbild zu IBM Watson Assistant
Technologie

IBM Watson Assistant

Watson Assistant identifies intention in requests that can be received via multiple channels. Watson Assistant is trained based on real-live requests and can understand the context and intent of the query based on the acting AI. Extensive search queries are routed to Watson Discovery and seamlessly embedded into the search result.
Blog 7/22/24

Let's build an Enterprise AI Assistant

In the previous blog post we have talked about basic principles of building AI assistants. Let’s take them for a spin with a product case that we’ve worked on: using AI to support enterprise sales pipelines.
Blog 4/16/24

The Intersection of AI and Voice Manipulation

The advent of Artificial Intelligence (AI) in text-to-speech (TTS) technologies has revolutionized the way we interact with written content. Natural Readers, standing at the forefront of this innovation, offers a comprehensive suite of features designed to cater to a broad spectrum of needs, from personal leisure to educational support and commercial use. As we delve into the capabilities of Natural Readers, it's crucial to explore both the advantages it brings to the table and the ethical considerations surrounding voice manipulation in TTS technologies.
Navigationsbild zu Data Science
Service

AI & Data Science

The amount of data that companies produce and process every day is constantly growing. This data contains valuable information about customers, markets, business processes and much more. But how can companies use this data effectively to make better decisions, improve their products and services and tap into new business opportunities?
Navigationsbild zu Data Science
Service

AI & Data Science

We offer comprehensive solutions in the fields of data science, machine learning and AI that are tailored to your specific challenges and goals.
Blog 2/3/25

ChatGPT & Co: LLM Benchmarks for January

Find out which large language models outperformed in the January 2025 benchmarks. Stay informed on the latest AI developments and performance metrics.
Kompetenz 2/26/25

AI - A technology is revolutionizing our everyday lives

For ARS, AI is an increasingly natural and organic part of software engineering. This is particularly true in cases where it is an integral part of applications and functions.
Wissen 4/30/24

GPT & Co: The best language models for digital products

Our analysis based on real benchmark data reveals which solutions excel in document processing, CRM integration, external integration, marketing support and code generation. Find your ideal model!

Wissen 8/30/24

LLM-Benchmarks August 2024

Instead of our general LLM benchmarks, we present the first benchmark of different AI architectures in August.
Blog 10/4/24

Open-sourcing 4 solutions from the Enterprise RAG Challenge

Our RAG competition is a friendly challenge different AI Assistants competed in answering questions based on the annual reports of public companies.

Blog 1/21/25

AI Contest - Enterprise RAG Challenge

TIMETOACT GROUP Austria demonstrates how RAG technologies can revolutionize processes with the Enterprise RAG Challenge.
Wissen 5/2/24

Unlock the Potential of Data Culture in Your Organization

Are you ready to revolutionize your organization's potential by unleashing the power of data culture? Imagine a workplace where every decision is backed by insights, every strategy informed by data, and every employee equipped to navigate the digital landscape with confidence. This is the transformative impact of cultivating a robust data culture within your enterprise.
Wissen 5/2/24

Unlock the Potential of Data Culture in Your Organization

Are you ready to revolutionize your organization's potential by unleashing the power of data culture? Imagine a workplace where every decision is backed by insights, every strategy informed by data, and every employee equipped to navigate the digital landscape with confidence. This is the transformative impact of cultivating a robust data culture within your enterprise.
Wissen 7/23/24

Graph Databases in the Supply Chain

The supply chain is a complex network of suppliers, manufacturers, retailers and logistics service providers designed to ensure the smooth flow of goods and information. The modern supply chain faces numerous challenges.
Wissen 7/23/24

Graph Databases in the Supply Chain

The supply chain is a complex network of suppliers, manufacturers, retailers and logistics service providers designed to ensure the smooth flow of goods and information. The modern supply chain faces numerous challenges.