#9 Daniel Engelhardt: Building a Company-Specific Retrieval Augmented Generation System (RAG)

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Founders Hub Berlin

Serop Baghdadlian

19 episodes

6 days ago

🎙️ Berlin Founders Hub Podcast 🌍 Welcome to the go-to podcast for Berlin’s most ambitious founders and creators and those building from anywhere! Hosted by the Berlin Founders Hub, we dive deep into interesting conversations with entrepreneurs, investors, and innovators inside and outside Berlin. 🚀 Real stories, honest challenges, and lessons from the front lines of startups. Whether you're launching your first venture or scaling your next big idea, this podcast is your backstage pass to the minds shaping the future. 📍Brought to you by Berlin’s dynamic founder community – where cowor

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#9 Daniel Engelhardt: Building a Company-Specific Retrieval Augmented Generation System (RAG)

Founders Hub Berlin

52 minutes 6 seconds

1 year ago

#9 Daniel Engelhardt: Building a Company-Specific Retrieval Augmented Generation System (RAG)

Summary

In this conversation, Daniel Engelhardt talked about the development and implementation of a Gen.ai RAG system designed to assist employees in a company.

He discusses the journey from ideation to product development, the technical architecture, the challenges faced, and the importance of prompt engineering.

Daniel shares insights on evaluating language model responses, security measures taken to protect sensitive information, and strategies to mitigate hallucinations in AI outputs.

The conversation also touches on the significance of data quality, measuring success through user engagement, and the cost considerations associated with deploying AI systems.

Takeaways

The Gen.ai RAG system aims to assist employees with company-specific tasks.
Prompt engineering is crucial and surprisingly complex in AI development.
Evaluating language model responses requires innovative approaches.
Security measures are essential to protect sensitive company data.
Mitigating hallucinations in AI outputs is a significant challenge.
Data quality directly impacts the effectiveness of AI systems.
User engagement is a key metric for measuring success.
Cost management is important but secondary to time savings.
Continuous feedback from users helps improve the AI system.
The integration of AI can enhance productivity and employee satisfaction.

Chapters

00:00 Introduction to Gen.ai and the Co-Pilot System

05:11 Development Journey: From Idea to Product

11:07 Technical Architecture and Scalability Challenges

17:04 Prompt Engineering: The Art of Asking Questions

22:28 Evaluating Language Model Responses

28:32 Security Measures in Internal Applications

33:54 Data Quality and Management Challenges

39:39 Measuring Success and User Engagement

44:59 Cost Considerations in AI Implementation

--------------------🤗Connect With Us🤗-----------------------

Connect with Serop Baghdadlian on LinkedIn: https://www.linkedin.com/in/serop-b-498332169/

Connect with Daniel Engelhardt on LinkedIn:

https://www.linkedin.com/in/danielengelhardt-entwickler/

Keywords

Gen.ai, AI applications, software development, prompt engineering, language models, data quality, user engagement, security measures, hallucinations, cost management