About
Created Internal LLM based upon Flan-UL2. The model achieves State-of-the-Art results on NLP tasks ranging from language generation, language understanding, text classification, question answering, commonsense reasoning, long text reasoning, structured knowledge grounding and information retrieval.
Challenge, approach, and impact
Optimization patterns
Customizing large language models for specific business problems is challenging because there are many options, and the approaches and language are often inconsistent. The choice comes down to optimizing the prompt for a general model or fine-tuning a model on your data.
Scaling prompt engineering
While it’s straightforward for simple use cases, when building production applications, you run into many challenges, such as ambiguity in how large language models ingest and generate results, compatibility of prompts between different large language models, and maintaining them.
How we built
Testimonials
Anonymous
Vice President of Data Science & ML
“Large Language Model (LLM) technology will play a significant role in the development of future applications. Why not start now?“
Team structure
Client team
***** *****
CEO
Project stakeholder
The client stakeholders were working closely with the team at Prime Holding
Agency team
1 x Vice President of Data Science & ML
Production