I'm working in a highly specialized medical field where general AI models struggle with our terminology. Is Prompt Engineering through "Few-Shot" learning enough to teach the model our specific coding standards, or do we need to look into more expensive RAG solutions to provide the necessary context for accurate diagnosis suggestions?
3 answers
Few-Shot prompting is excellent for showing the model the "format" and "style" you expect, but it has limitations with "knowledge." In a medical setting, you should provide 3-5 high-quality examples of a symptom-to-code mapping within the prompt. However, if your terminology changes frequently or requires referencing massive medical libraries, Few-Shot alone will fail because you'll run out of "context window" space. I recommend a hybrid approach: use RAG to pull the most relevant medical whitepapers, and then use Few-Shot prompting to tell the AI exactly how to summarize that specific information into your required format.
When you're doing Few-Shot for medical data, how are you ensuring that the examples you put in the prompt don't contain sensitive patient info (PHI) that might end up in the model's logs?
Don't forget that the order of your examples matters! Models often have a "recency bias," where they pay more attention to the last example you gave them than the first one.
That's a very subtle but important point, Daniel. I've seen prompts fail just because the most complex example was buried in the middle instead of being placed at the end.
We use strictly "synthetic" data for our Few-Shot examples. These are fictionalized cases that mirror real-world complexities but contain zero actual patient identifiers. It takes a bit more time to write these "perfect" examples, but it's the only way to stay HIPAA compliant while still giving the model a clear template to follow. We also use a local, self-hosted LLM for the initial processing to ensure no data ever leaves our internal secure network, which is a major requirement for our legal team.