From: redpointai
Starting small and working iteratively is crucial when approaching AI projects [00:00:01]. Any progression in complexity should be justified by rigorous Return on Investment (ROI), ensuring that efforts contribute to meaningful objectives [00:00:02].
Iterative Testing and Benchmarking
A key part of the experimental process involves building benchmarks and testing against them [00:00:08]. It’s common to find initial benchmarks are inadequate, necessitating the creation of better ones [00:00:09].
Litmus Testing with Minimal Investment
The journey often begins with a minimal financial outlay, such as spending 20 cents on platforms like OpenAI or Llama on Databricks [00:00:12]. This initial investment serves as a litmus test to determine if AI is suitable for a given task [00:00:16]. There is currently limited predictability regarding an AI model’s effectiveness for a specific use case [00:00:19].
The Scientific Approach
Approaching AI implementation should be seen as a scientific endeavor, akin to data science [00:00:25]. The recommendation is to conduct experiments to find out what works [00:00:27].
To give an experiment the best chance of success:
- Try it on the best possible model available [00:00:31].
- Start by simply prompting the model [00:00:34].
- Initially, manually provide relevant documents directly into the context, rather than immediately implementing complex Retrieval Augmented Generation (RAG) systems [00:00:34].
- Observe the results to determine if the approach yields a valuable outcome [00:00:41].
Scaling Up and Advanced Techniques
Once initial value is demonstrated, the next step is to progressively increase complexity [00:00:43].
Retrieval Augmented Generation (RAG)
If manual context provision proves effective, the next step might be implementing “hardcore RAG” [00:00:45]. This becomes necessary when the model requires access to specific internal enterprise data that it would not inherently know [00:00:47].
Fine-tuning
If significant value is derived from the AI application, fine-tuning the model can be considered [00:00:53]. Fine-tuning allows specific knowledge to be baked into the model, potentially leading to better quality outputs [00:00:55]. While fine-tuning incurs a higher upfront cost, it can yield improved performance in the long run [00:00:57]. This iterative and experimental approach is part of the creative process and experimentation with AI [00:00:27].