I’m Christoffer from Oslo, Norway. I thrive in fast-paced environments and enjoy learning new things. At CV Partner, 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.
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.
I wrote my bachelor’s thesis at CV Partner on using machine learning to enhance consultant selection. This project sparked my interest in applying AI in innovative ways within our product.
I’m involved in experimenting with different models and ensuring our production features work as intended. We’re exploring how embeddings can enhance CV Partner’s daily use and feature set. I also work on unit testing prompts to ensure their effectiveness.
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.
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.
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.
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.
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.
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.
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.
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.
I’m excited about the open-source community developments for Large Language Models and exploring new features with embeddings in CV Partner.
Understand that LLMs may not suit every task. Combining LLMs with embeddings and using techniques like RAG can enhance their effectiveness.
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.
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.
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.
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.