From: redpointai
Adopting AI solutions should follow an incremental approach, starting small and gradually scaling up, with each step justified by rigorous evaluation [00:00:01]. The focus must be on making progress on objectives that truly matter [00:00:06].
Incremental Approach to AI Adoption
A crucial aspect of this process involves rigorous ROI analysis [00:00:02]. It’s essential to test it with benchmarks and accept that initial benchmarks may be inadequate, requiring continuous refinement [00:00:08].
Experimentation and Initial Testing
The journey typically begins with minimal investment, perhaps spending as little as 20 cents on platforms like OpenAI or Llama, to conduct a “litmus test” [00:00:12]. This initial phase aims to determine if AI is going to be good at this particular task [00:00:17]. There is currently limited good predictability regarding AI’s suitability for specific use cases [00:00:19].
It is recommended to approach AI implementation as a literal data science experiment [00:00:25]. To run an experiment, users should aim to maximize their chances of success by:
- Utilizing the best possible model available [00:00:31].
- Starting with simple prompting, or manually providing a few known helpful documents into the context, to observe initial outcomes [00:00:34].
Escalating AI Application Complexity
If initial experiments yield promising results, the next steps involve increasing complexity:
- Retrieval Augmented Generation (RAG): If the model lacks “telepathy” regarding internal enterprise data, implementing RAG becomes necessary to integrate proprietary information [00:00:45].
- Fine-tuning: If significant value is being derived, fine-tuning can be considered [00:00:53]. This process bakes knowledge directly into the model, potentially incurring higher upfront costs but leading to better quality [00:00:55].