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.

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Martin Warnung

Sales Consultant TIMETOACT GROUP Österreich GmbH +43 664 881 788 80

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Common Mistakes in the Development of AI Assistants