From: redpointai
When embarking on AI projects, a recommended approach is to always start small and progressively build up [00:00:01]. This incremental advancement should be rigorously justified by ROI (Return on Investment), ensuring that progress is made on aspects that truly matter [00:00:02].
The Experimental Mindset
AI development should be viewed as a scientific endeavor, akin to data science in its literal sense [00:00:25]. It’s often challenging to predict whether AI will be effective for a specific use case [00:00:18]. Therefore, the strategy is to run experiments and try things out [00:00:28].
Initial Steps for Experimentation
The journey can begin with minimal investment, such as spending a small amount (e.g., 20 cents) on platforms like OpenAI or Llama on DataBricks [00:00:12]. This allows for a “litmus test” to gauge AI’s suitability for a task [00:00:16].
To maximize the chance of success, one should:
- Utilize the best possible AI model available [00:00:31].
- Start with simple prompting, or manually provide a few relevant documents into the context to see the results [00:00:34].
The goal at this stage is to determine if there’s any value or “there there” before investing further [00:00:41].
Iterative Scaling and Refinement
Once initial value is established, the project can scale up:
- Advanced Retrieval-Augmented Generation (RAG): If initial tests show promise, the next step might involve implementing more sophisticated RAG techniques to integrate proprietary data, as models cannot inherently access internal enterprise data [00:00:45].
- Fine-tuning: If significant value is being derived, fine-tuning the model can be considered <a class=“yt=“yt-timestamp” data-t=“00:00:53”>[00:00:53]. While this involves higher upfront costs, it often leads to improved quality [00:00:57].
Benchmarking for Progress
A crucial part of this iterative process involves building and testing benchmarks [00:00:08]. It’s common to find that initial benchmarks are inadequate, necessitating the development of better ones over time [00:00:09].