Can you provide a brief introduction about yourself and your role at Flowcase?
I’m Christoffer from Oslo, Norway. I thrive in fast-paced environments and enjoy learning new things. At Flowcase, I develop AI features and handle data imports. My focus is on ensuring our AI features work well and exploring new ones for our customers. I also help with onboarding and integrating data.
How did you get started with AI and prompt engineering?
I pursued a bachelor’s in intelligent systems, combining embedded systems and AI. My interest grew as large language models advanced, leading me to apply this technology in real-world products.
What attracted you to work with data import processing and AI technologies?
I wrote my bachelor’s thesis at Flowcase on using machine learning to enhance consultant selection. This project sparked my interest in applying AI in innovative ways within our product.
Can you describe your current responsibilities and projects related to AI at Flowcase?
I’m involved in experimenting with different models and ensuring our production features work as intended. We’re exploring how embeddings can enhance Flowcase’s daily use and feature set. I also work on unit testing prompts to ensure their effectiveness.
How has AI, specifically generative AI, been integrated into Flowcase’s solutions?
We use Large Language Models for translations and text proofreading. Additionally, we’re developing features utilizing embeddings and Retrieval-Augmented Generation (RAG) to generate text.
Could you share some highlights from your recent presentation at the Amazon Web Services event in Stockholm?
Nicolai, our CPO, and I presented on “Model Choice in Amazon Bedrock.” We discussed our approach to generative AI, sharing insights on implementing advanced technologies.
What key points did you discuss regarding experimenting, evaluating, and building with foundation models using Amazon Bedrock?
We shared our experiences with different models on Amazon Bedrock, highlighting our journey with Amazon Titan Text Embeddings and Anthropic Claude 3. We explained our evaluation process and how specific features depend on these models.
What criteria and methodologies do you use to evaluate the best foundation models for your use case?
We use a multi-step evaluation process with a “test-suite” to compare language models. Classic tools and methods measure retrieval and ranking performance, helping us identify the most suitable models.
Can you walk us through the process you followed to integrate generative AI into Flowcase’s solutions using Amazon Bedrock?
We designed middleware to interface with Amazon's Bedrock API, allowing us to upgrade and test new models easily. This approach ensures models respond accurately to our queries.
What were some of the biggest challenges you faced during this integration process, and how did you overcome them?
Ensuring models produce precise outputs was crucial for compliance and user trust. We implemented manual quality checks and multi-shot prompting techniques. Validating JSON outputs and using low temperature settings helped maintain output consistency.
What tangible benefits has Flowcase seen from integrating generative AI into its solutions?
Generative AI has improved automatic translations and advanced proofreading, providing high-quality outputs. Customers have praised our translation feature as a game-changer compared to tools like Google Translate.
What are your future plans or next steps for AI development at Flowcase?
We’re exploring more AI use cases while maintaining accuracy and compliance. Our goal is to help users keep their resumes up-to-date and develop AI-driven tools for bid teams. We have new features in closed beta and are excited to enroll more companies.
Are there any upcoming projects or initiatives involving AI that you are particularly excited about?
I’m excited about the open-source community developments for Large Language Models and exploring new features with embeddings in Flowcase.
What advice would you give to other professionals looking to integrate AI into their business solutions?
Understand that LLMs may not suit every task. Combining LLMs with embeddings and using techniques like RAG can enhance their effectiveness.
How do you balance the technical challenges of AI development with the practical needs of our users?
We involve users in the development process, soliciting feedback to ensure features meet their practical needs. This collaborative approach helps us innovate responsibly and deliver solutions that improve efficiency and meet diverse user requirements.
Can you share any interesting anecdotes or experiences from your work with AI that others might find inspiring or insightful?
Navigating the balance between innovation and caution is key. Rigorous testing and user feedback help us implement AI responsibly, ensuring it enhances user experience without compromising data integrity.
How do you stay updated with the latest advancements and trends in AI technology?
I read extensively, follow academic courses, and engage with forums like r/LocalLLaMA and HackerNews. Connecting with industry leaders on LinkedIn also keeps me informed.
What resources or learning materials would you recommend for those interested in AI and prompt engineering?
The anthropic models' cookbook and OpenAI's prompt engineering guide are invaluable. Starting prompts in a way that guides the desired output can lead to more consistent results.
