Our AI-powered knowledge base combines three powerful technologies—vector embeddings, large language models (LLMs), and prompt engineering. By using vector embeddings, we organize your documents in a way that captures their true meaning, so the system can retrieve exactly what you need, fast. LLMs help the AI go beyond basic keyword searches, delivering smarter, context-aware insights. And with advanced prompt engineering, we fine-tune how the AI responds, making sure it gives you the most accurate, relevant information every time. It’s all about giving you the best possible experience with your data.
Vector Store
We use a vector store to organize your documents by their underlying meaning, not just keywords. This helps the AI make connections across different types of data, pulling insights from even the most unrelated documents to give you a complete, accurate picture of your information.
Rathe that just dumping the data into the store, Tulip’s AI organises the vector into a representation that will make the most sense for your use case.
LLM tuning
We tune large language models (LLMs) to understand your specific data and business needs. This ensures the AI delivers more accurate, context-relevant insights, rather than generic responses. By fine-tuning the LLM, we make sure the knowledge base aligns perfectly with your goals and delivers real value.
Most companies just throw something together with LangChain and hope for the best. We’ve adapted our systems to tune the LLM in the best way possible for your needs.
Prompt Engineering
Prompt engineering shouldn’t be a guessing game of random text strings. Tulip AI’s technology uses programmatic machine learning techniques to fine-tune prompts, ensuring they get the best responses from both the LLM and vector store. This approach guarantees accurate, efficient results tailored to your specific data and needs.
The prompts are contnuously monitored and updated to ensure they stay consistent with the data you have available.