From: redpointai
When integrating AI models with enterprise data, a strategic, iterative approach is recommended [00:00:01]. The journey should be justified by rigorous Return on Investment (ROI) to ensure progress on meaningful objectives [00:00:03].
Initial Experimentation and Benchmarking
Begin with small-scale experiments, such as spending a minimal amount on platforms like OpenAI or Llama on Databricks, to litmus test if AI is suitable for a particular task [00:00:12]. There is often low predictability regarding whether AI will be effective for a given use case [00:00:19].
It is advised to act as a data scientist: run experiments [00:00:25]. This involves:
- Starting with the best available model [00:00:31].
- Directly prompting the model or manually providing helpful documents into the context to observe outcomes [00:00:34].
- Developing benchmarks, testing against them, and iteratively improving them [00:00:08].
Scaling Up with RAG (Retrieval Augmented Generation)
After initial experiments confirm value, the next step involves implementing more robust data integration methods [00:00:41]. This often means utilizing Retrieval Augmented Generation (RAG) to explicitly bring enterprise data to the model, as the model cannot inherently know about internal enterprise data [00:00:45].
Fine-tuning for Enhanced Quality
Fine-tuning an AI model is a subsequent step, to be considered once initial value has been demonstrated [00:00:51]. Fine-tuning allows for baking specific knowledge and patterns into the model [00:00:54]. While it involves a greater upfront cost, fine-tuning can lead to significantly better quality outcomes [00:00:57].