Prompt Like a Pro - Best Prompting Tips for LLMs

Summary

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Episode Overview

Podcast: The Health AI Brief
Host: Stephen A
Episode: Prompt Like a Pro – Best Prompting Tips for LLMs
Date: May 14, 2026

This episode gives busy healthcare professionals an ultra-concise, actionable guide to crafting effective prompts for large language models (LLMs) in clinical settings. Host Stephen A breaks down a repeatable "mega-prompt" formula for getting reliable, high-quality AI output—demonstrating how prompt structure can powerfully shape results, reduce ambiguity, and save time.

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Key Discussion Points & Insights

1. The Anatomy of a "Mega-Prompt" (00:12 – 02:05)

• Prompt Engineering as a Tool:
  "A good clinical prompt isn't just a sentence, it's a piece of engineering to get consultant level output every time." (A, 00:18)
• Common Mistake:
  Inconsistent AI results usually come from missing key structural elements in the prompt.
• The Five Essential Components:
  1. Role: Specify the AI’s persona or expertise.
     Example: "You're a highly specialist oncologist with decades of experience treating this specific haematological malignancy."
  2. Context: State the scenario or background.
     Example: "I'm preparing a summary for a multidisciplinary team meeting regarding a new diagnosis of lung cancer."
  3. Task: Define the specific action required.
     Example: "Summarize this attached report."
  4. Constraints: Limitations or requirements for the response.
     Example: "Do not mention incidental findings, only use British English, limit to 200 words."
  5. Output Format: Prescribe structure and style.
     Example: "Present this as a bulleted list with a final recommendation section."

2. Why Structure Works (02:05 – 03:00)

• Analogy to Clinical Practice:
  "Think of it like an SBAR handover. SBAR works because it's a mental model that ensures no important information is forgotten. The megaprompt formula does the same for the AI." (A, 02:15)
• Outcome:
  Defining all five areas reduces ambiguity, leading to more accurate and relevant AI outputs.

3. High-Yield Prompting Tips (03:00 – 04:00)

Key Takeaways:

• Modularize Your Prompts:
  Build a "master template" in a notes app using the five headers (Role, Context, Task, Constraints, Output Format) for rapid reuse.
• "Context is King":
  "Never assume the AI knows the setting—explicitly state: This is for a GP referral, this is for an internal ward note, this is for some research I'm doing." (A, 03:25)
• Control the Output:
  "Most AI frustration comes from bad formatting—being very specific about the output format, for example, a table with three columns, saves you minutes of reformatting yourself later." (A, 03:40)

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Notable Quotes & Memorable Moments

• "If you find yourself getting inconsistent results from an AI, it's usually because the prompt's missing a structural pillar." (Stephen A, 00:10)
• "A good clinical prompt isn't just a sentence, it's a piece of engineering..." (Stephen A, 00:18)
• "Think of it like an SBAR handover. SBAR works because it’s a mental model... The megaprompt formula does the same for the AI." (Stephen A, 02:15)
• "Context is king. Never assume the AI knows the setting—explicitly state..." (Stephen A, 03:25)
• "Most AI frustration comes from bad formatting—being very specific about the output format... saves you minutes of reformatting yourself later." (Stephen A, 03:40)

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Important Timestamps

| Timestamp | Segment Description | |-----------|------------------------------------------------------------| | 00:00 | Introduction to clinical-grade prompt engineering | | 00:12 | The five essential components of a "mega-prompt" explained | | 02:15 | SBAR handover analogy to prompt structure | | 03:10 | Key takeaways and master prompt strategies | | 03:40 | Output formatting tip for time-saving |

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Summary Table: The Mega-Prompt Formula

| Component | Example (from episode) | |-----------------|------------------------------------------------------------| | Role | Highly specialist oncologist, decades of experience | | Context | Summary for MDT on new lung cancer diagnosis | | Task | Summarize attached clinical report | | Constraints | Ignore incidental findings, use British English, ≤200 words | | Output | Bullet points + final recommendation section |

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Episode Recap

Stephen A delivers a compact, practical guide to mastering prompts for LLMs in the clinic. By treating prompts as structured, modular templates (just like an SBAR in medicine), clinicians and health leaders can ensure AI tools deliver consistent, clinically relevant output—saving time, reducing frustration, and boosting the ROI of health tech adoption.

Transcript

[A]

Welcome to the Health AI Brief, Breaking down the AI shaping our world one concept at a time. If you find yourself getting inconsistent results from an AI, it's usually because the prompt's missing a structural pillar. A good clinical prompt isn't just a sentence, it's a piece of engineering to get consultant level output every time you need a repeatable formula. What are the five essential components of a kind of mega prompt? The formula is role, context, task constraints and output. We've discussed a lot of this in previous episodes, but here we're putting it all together into one clear summary. So first is role Specify. You're a highly specialist oncologist with decades of experience treating this specific haematological malignancy. Second is context. I'm preparing a summary for a multidisciplinary team meeting regarding a new diagnosis of lung cancer. Third is the task. Summarize this attached report. Fourth, constraints do not mention incidental findings, only use British English, limit to 200 words. And fifth is the output format. Present this as a bulleted list with a final recommendation section. Now, these examples aren't necessarily ones that you should be using in just any language model, particularly if they're pertaining to patient data, but they're used as an example. Think of it like an SBAR handover. SBAR works because it's a mental model that ensures no important information is forgotten. The megaprompt formula does the same for the AI. By defining these five areas, you remove potential ambiguity that leads to generic or incorrect answers. So the key takeaways are firstly, to modularize your prompts, build a master template in a notes app that you can use for any prompts using these five key headers. Second is that context is king. Never assume the AI knows the setting it explicitly state. This is for a GP referral. This is for an internal ward note. This is for some research I'm doing. Providing as much context as possible removes that ambiguity. And third is control the output. Most AI frustration comes from bad formatting being very specific about the output format. For example, a table with three columns saves you minutes of reformatting yourself later. So that's the megaprompt formula in a nutshell. If you if you found that useful, don't forget to hit like and subscribe. We're going to be covering much more topics on these sorts of things so that you don't miss out.