Friday, April 11, 2025

AI Watch: Google Publishes 68-page Booklet on Prompt Engineering (For Using AI)

Google publishes a 68-page booklet on the science and art of "prompt engineering," that is, interacting with AI to accomplish goals.

https://www.kaggle.com/whitepaper-prompt-engineering

Google has an auto podcast (19 min) about the book here:

https://www.youtube.com/watch?v=F_hJ2Ey4BNc

  • Note that the booklet is designed for a particular interface to Google Gemini, and, it is written for pretty advanced users.   
  • BUT there is still much to be gleaned for less-advanced users, as you can look over the shoulders of professionals and see how experts interact with AI via prompts.


 See an extensive "AI CORNER" below.

###

AI CORNER

Here’s an AI review of the 2025 Google Guide to Prompt Engineering by Lee Boonstra, tailored for your genomic policy blog readers who also follow AI developments:

🔍 Worth Your Download?

Yes—if you use, tune, or even think about large language models (LLMs) in your work.
This is Google’s internal playbook for how to write prompts that actually work. It’s presented accessibly but backed by deep product and engineering expertise from teams working on Gemini and Vertex AI.

📘 What the Paper Covers

This 68-page guide serves as both a primer and a cookbook for crafting prompts that optimize performance, reliability, and safety in LLMs. It walks you through:

  • Prompt formats (zero-shot, few-shot, chain-of-thought, system/role/contextual prompting)

  • Sampling controls (temperature, top-K, top-P)

  • Advanced strategies like:

    • Step-back prompting

    • Tree of Thought

    • ReAct prompting (LLM + tools = baby agents)

    • Self-consistency voting

    • Automatic prompt engineering

🧠 Highlights & Takeaways

  • Prompting is engineering, not guessing. Each example (whether for classifying a movie review or writing Python code) comes with clear configurations and an explanation of why it works.

  • LLMs are stochastic, not deterministic. You’ll get repeatable, better output when you configure sampling controls like temperature, top-k, and top-p in combination—not in isolation.

  • Chain-of-Thought and Step-Back prompting are now table stakes for complex reasoning tasks.

  • Few-shot examples aren't just filler—they’re essential for guiding structure, logic, and tone.

  • Best practices include using positive instructions over constraints, setting token limits smartly, and mixing up class order in classification prompts to reduce overfitting.

  • Debugging code with LLMs? The guide walks through real examples, from bash-to-python translations to catching bugs using self-generated logs and prompts.

🧪 Why It’s Relevant to Genomics and Policy

Even if you're not writing code:

  • Prompt engineering controls reproducibility and reduces hallucinations, two themes familiar to diagnostics labs.

  • The system/role/contextual prompts mirror clinical decision pathways, which could be useful when working with AI to parse literature, generate SOPs, or synthesize regulatory language.

  • As LLMs are deployed in diagnostics, good prompt design becomes a compliance issue, not just a UX feature.

📎 For Power Users and Curious Analysts Alike

Whether you’re validating a new clinical NLP tool, training a chatbot for patient triage, or using GPT to summarize MolDx LCDs, this guide shows you how to fine-tune your input for consistent, policy-aligned output.

Bottom Line:

Google’s Prompt Engineering Guide is the best structured, most readable, and most example-rich guide to LLM prompting as of 2025.
You’ll come away with actual copy-pasteable strategies and a mental model of how prompts, model settings, and outputs interact.

Let me know if you'd like a visual cheat sheet or a one-page summary handout for internal staff or clients.

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OK. Let's assume you use AI 30 minute  a day in your work, for the past year, so you're pretty experienced, but self taught.    What are 10 things you will find both NEW and also USEFUL in this book?

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Great framing. Assuming I’m an experienced, non-professional AI user (e.g., using ChatGPT, Gemini, Claude, or Perplexity 30–60 minutes daily for tasks like research, email drafts, code help, writing assistance, brainstorming), here are ten concepts from the 2025 Google Prompt Engineering Guide that would be both new to me and valuable:

🔟 Ten Valuable New Concepts

  1. Step-Back Prompting
    🔄 Instead of solving a task directly, first prompt the model to consider general principles or patterns, then use that response to guide the specific task.

    Why it’s useful: Helps with tasks that require reasoning or “thinking outside the box.” Great for strategy, synthesis, or creative writing.

  2. Self-Consistency Prompting
    🗳️ Send the same Chain-of-Thought prompt multiple times at high temperature, then “vote” on the most common final answer.

    Why it’s useful: Makes your answers more robust and reduces hallucinations in complex reasoning tasks—especially for ambiguous queries.

  3. Tree-of-Thought Prompting (ToT)
    🌳 Instead of a linear Chain-of-Thought, this method explores multiple reasoning branches before converging on a solution.

    Why it’s useful: Helps in complex problem-solving, planning, or design work where multiple valid paths exist.

  4. ReAct Prompting (Reason + Act)
    🤖 Combines internal reasoning with external tool use—like search APIs or code execution—to improve real-world answers.

    Why it’s useful: Gives you a basic agent-like workflow without needing to build an agent. Perfect for market research or current events queries.

  5. System / Role / Contextual Prompting as Distinct Modes
    🧩 Clear separation of three prompt layers: system (what the model should do), role (who it's acting as), and context (the data it needs).

    Why it’s useful: Lets you tune tone and style (e.g., make the model act like a physician or a 5th-grade teacher), and also better structure your multi-part prompts.

  6. Prompt Variables for Reusability
    🧠 Use {city}, {disease}, {product} etc. inside prompts so they can be reused dynamically across tasks or in apps.

    Why it’s useful: Makes it easy to automate AI workflows or build prompt libraries—saves time in daily use.

  7. Output Control via Sampling Configuration
    🎛️ Combining temperature, top-K, top-P, and max tokens effectively—not just using one slider at a time.

    Why it’s useful: You gain much better control over creativity vs. reliability (e.g., using temperature 0 + top-P 0.9 for deterministic classification tasks).

  8. JSON Prompting for Safer, Structured Output /   JavaScript Object Notation. 
    🧾 Ask the model to return its answers in a strict JSON schema—even showing the format in the prompt.

    Why it’s useful: Helps with automation and reduces hallucination—critical if you're using AI for data extraction or to pass into other tools.

  9. Automatic Prompt Engineering (APE)
    🤯 Use the model itself to generate better prompts, then score and select the best-performing one.

    Why it’s useful: You can "meta-prompt" your way to better performance in novel or high-risk tasks (e.g., regulatory writing, hypothesis generation).

  10. Role Prompting with Tone/Style Modifiers
    🎭 Assign roles and style at once (e.g., “You are a humorous travel guide who speaks informally.”)

Why it’s useful: Saves you from endlessly rewriting tone—great for brand voice, presentations, or teaching materials.


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Let's say you work in reimbursement consulting for investors, genomisc labs, and write white papers in the field.   ???

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Perfect—thanks for that context. Here are ten things from the Google Prompt Engineering Guide that would be new and valuable specifically for a genomics policy consultant like you, especially in fundraising strategy, investor due diligence, and white paper development:

🔟 Prompt Engineering Insights for Genomics Industry Consulting

  1. System/Role/Context Prompt Layering
    🧩 Explicit separation of “what the model should do,” “who it’s acting as,” and “what background data it has.”

    Use case: Draft a report where the model behaves like an oncology investor relations officer or a regulatory reviewer—shifting tone, vocabulary, and depth accordingly.

  2. Step-Back Prompting for Landscape Synthesis
    🔄 Ask the model to summarize general trends (e.g., "What are the components of a successful MRD test launch?") before writing your actual white paper content.

    Use case: Improves strategic framing in pitch decks and executive summaries—forces a broader view before specifics.

  3. Chain-of-Thought Prompting for Claim Support
    🧠 Walks the model through intermediate reasoning steps, showing how it links data, citations, or regulatory events.

    Use case: Helps when you’re comparing payor coverage criteria for LDTs vs. PMA tests, or modeling reimbursement timelines.

  4. Self-Consistency Sampling for Investment Theses
    🗳️ Run a CoT prompt 5+ times at high temperature, then find the dominant investment thesis or forecast.

    Use case: Reduce bias or cherry-picking when generating multiple perspectives for a strategic options memo.

  5. Prompt Variables for Reusable Due Diligence Templates
    🔁 Write prompts with placeholders like {company}, {assay}, {FDA status} to generate fast turnaround analyses.

    Use case: Quickly switch focus from a ctDNA MRD company to a methylation early detection firm without rewriting core frameworks.

  6. Few-Shot Prompting with Counterexamples
    📊 When evaluating pitch language, regulatory claims, or clinical utility arguments, provide “good” and “bad” versions.

    Use case: Great for training junior team members or creating investor training materials—e.g., what not to say in an MRD pitch.

  7. ReAct Prompting for Competitive Intelligence
    🌐 Combine LLM reasoning with external tools like search to assess, say, “recent PMA approvals in liquid biopsy.”

    Use case: Prototype lightweight research agents that triangulate public FDA, CMS, and investor data in one query loop.

  8. Structured JSON Outputs for Investment Memos  / JavaScript Object Notation. 
    🧾 Prompt the LLM to output a JSON with structured fields like {clinical utility}, {reimbursement}, {competitive landscape}.

    Use case: Automates memo population and lets you export data directly into Excel or investor briefing templates.

  9. Instruction-Over-Constraint Framing
    ✅ Instead of “Don’t talk about reimbursement,” say “Summarize investor risks without addressing reimbursement issues.”

    Use case: Better control when shaping nuanced LLM output (e.g., evaluating MRD platforms where coverage is still pending).

  10. Multimodal Prompt Planning
    🎥 Anticipates combining text prompts with graphics, tables, or datasets (e.g., inputting a Kaplan-Meier curve or coverage policy PDF).

Use case: As LLMs integrate visual inputs, you’ll prompt them to summarize data slides or parse a Medicare LCD directly.

Let me know if you’d like a tailored prompt library for genomics consultants, or a slide summarizing these for your next investor pitch workshop.

##

Prompt library.

##


 Absolutely. Here’s a Tailored Prompt Library for Genomics Consultants, based on the 10 insights we discussed. These are real-world, reusable prompt templates designed for your work in:

  • 🧬 Genomics policy

  • 💰 Fundraising strategy

  • 📊 Investor due diligence

  • 📄 White paper development

🧠 Prompt Library: Genomics Consulting Edition

Each entry includes:

  • Use Case

  • Prompt Template

  • Tips for Use

1. Positioning a Company for Investors (System + Role Prompting)

Use Case: Write a compelling intro to a pitch deck or white paper.

Prompt:

Act as a biotech investor relations officer. Write a one-paragraph summary introducing {company_name}, a genomics company specializing in {assay_type}. The summary should emphasize clinical need, competitive edge, and addressable market.

Tip: Replace {company_name} and {assay_type} with client details.

2. Fundraising Framing via Step-Back Prompting

Use Case: Start a white paper with strong strategic framing.

Prompt:

Before analyzing {company_name}, summarize the top 3 unmet needs or bottlenecks in the field of {domain}, such as MRD testing, ctDNA, or methylation detection. Then use that to frame the company’s solution.

Tip: Great for executive summaries or slide 2 of a deck.

3. Coverage and Reimbursement Reasoning (Chain of Thought)

Use Case: Generate a walkthrough of reimbursement risks.

Prompt:

You are a Medicare reimbursement expert. A genomic test has FDA approval but limited Medicare LCD coverage. Think step-by-step about the hurdles this test will face for nationwide reimbursement and how a company might overcome them.

Tip: Use when planning payor strategy timelines.

4. Compare Companies with Self-Consistency Sampling

Use Case: Generate multiple investment perspectives, then pick the best.

Prompt:

Compare Guardant Health and Personalis in the MRD space. What are the key strategic differences in their approach to clinical evidence, FDA approval, and payer strategy? Let’s think step-by-step.

Tip: Run 3–5 times and choose the most consistent or compelling answer.

5. Automated Due Diligence Memos with Prompt Variables

Use Case: Quickly switch from company to company.

Prompt:

Summarize the clinical value, commercial traction, and regulatory status of {company_name}, which offers a {assay_type} assay targeting {clinical_indication}. Focus on unmet need, FDA status, and payor engagement.

Tip: Plug into Airtable, Notion, or Google Sheets for batch outputs.

6. Messaging Calibration (Few-Shot Prompting with Good/Bad Examples)

Use Case: Train teams or refine pitch language.

Prompt:

Below are examples of how to describe a genomic test to payers. Which is stronger, and why? Then write a better version:
Example A: "We believe our test is helpful."
Example B: "Our test demonstrates 95% sensitivity for recurrence detection in early-stage CRC patients, with peer-reviewed validation."

Tip: Add your own examples to personalize.

7. Market Landscape via ReAct Prompting

Use Case: Combine LLM + real-time search for competitive intelligence.

Prompt:

How many FDA-approved liquid biopsy MRD tests exist in the U.S. market as of 2025? Search for public announcements and approvals, then summarize key players and test types.

Tip: For tools like Perplexity, Claude with browsing, or LangChain agents.

8. Structured Memo Output (JSON Prompting)

Use Case: Auto-populate due diligence templates.

Prompt:

*Summarize {company_name} in this format:

{
  "clinical_utility": "",
  "regulatory_status": "",
  "reimbursement_outlook": "",
  "competitive_advantage": "",
  "investor_risks": ""
}
```*

**Tip**: Outputs can be dropped into Airtable, Excel, or merged into slides.

---

### 9. **Instruction Over Constraint Prompting (Tone Shaping)**  
**Use Case**: Avoid ambiguity or over-hallucination.

**Prompt**:
> *Write a three-paragraph section for an investor memo about {company_name}. Emphasize product-market fit and FDA status. Do not discuss reimbursement strategy.*

**Tip**: Emphasize what *to* include rather than just what *not* to.

---

### 10. **Image or Slide Interpretation (Future Multimodal Prompt)**  
**Use Case**: Explain a chart or K-M curve for a genomics test.

**Prompt**:
> *Here is a Kaplan-Meier survival curve from a clinical trial of {company_name}’s MRD test in NSCLC. Summarize what it shows about test performance and how it supports the clinical utility claim.*

**Tip**: Will be useful in GPT-4 Vision, Gemini 1.5, and multimodal models.

---

Would you like this as a **Google Doc**, **Markdown**, or **Notion database**?

Or, I can add **a section with prompts tailored for slide decks** and **investor FAQs** next.



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For users of SCITE.AI, see an 18 page guide:
https://www.researchsolutions.com/ebook-aiprompts-academic