Prompt Engineering Exercises
Summarizing a document - One valuable use of AI would be to summarize
a long report or document so that you can get a high level understanding of
the content without having to read through the entire document.
Let's take the latest Nvidia earnings release.
I can instruct Gemini to summarize the text from a specific link.
First, I’ll ask Gemini to find the the link I need
need link for the latest earnings release from Nvidia
Then I will get Gemini to summarize the link
SUMMARIZE THE TEXT FROM THIS LINK -
NVIDIA reported record-breaking financial results for the fourth quarter and
full fiscal year 2026, driven by the "exponential" growth of agentic AI
and accelerated computing.
Financial Highlights
Q4 Revenue: Record $68.1 billion, up 73% year-over-year and 20%
from the previous quarter.
Full-Year Revenue: Record $215.9 billion, a 65% increase from fiscal 2025.
Earnings per Share (EPS): * GAAP: $1.76 (up 98% YoY).
Non-GAAP: $1.62 (up 82% YoY).
Gross Margin: Remained strong at 75.0% (GAAP) for the quarter.
Shareholder Returns: NVIDIA returned $41.1 billion to shareholders
in fiscal 2026 through share repurchases and dividends.
Segment Performance
Data Center: The primary growth engine, reaching a record $62.3 billion
in Q4 revenue (up 75% YoY). This was driven by the shift to
AI infrastructure and the ramp-up of the Blackwell platform.
Gaming: Q4 revenue was $3.7 billion (up 47% YoY), supported by
strong Blackwell demand, though down 13% sequentially due to
post-holiday inventory normalization.
Professional Visualization: Revenue rose to $1.3 billion
(up 159% YoY).
Automotive and Robotics: Achieved record full-year revenue of
$2.3 billion (up 39% YoY).
Future Outlook & Strategic Moves
Q1 FY2027 Guidance: NVIDIA expects revenue of $78.0 billion (± 2%).
New Platforms: CEO Jensen Huang highlighted the arrival of the
Rubin platform (the successor to Blackwell), which aims to reduce AI
inference costs by up to 10x.
Partnerships: The company announced major collaborations with
Meta (deploying millions of GPUs), AWS, Microsoft Azure, and
Anthropic.
Policy Change: Starting in Q1 FY2027, NVIDIA will include stock-based
compensation in its non-GAAP financial measures.
CEO Quote: Jensen Huang stated, "The agentic AI inflection point has arrived...
Our customers are racing to invest in AI compute —
the factories powering the AI industrial revolution."
Earnings call
Next I want to get the sentiment from the earnings call related to the press release.
Python Code
First I ask gemini to find the latest earnings transcript I want to analyze
link for the latest nvidia conference call from motley fool
Note: I need to use motley fool because they do not block API access
As a Google cloud developer I use AI to help speed up the process or debug
issues in code. I can use AI to either generate new code or change existing
code based upon parameters that I provide. Staying with the same Example,
ask the model to provide document and paragraph level sentiment for
an earnings call.
I set the persona
python developer
Task
build a program using google cloud nlp libraries to extract the document
and paragraph level sentiment scores
Format
the output should be in JSON format
Constrait
limit paragraph scores to first 10 paragraphs
As a python developer build a program using google cloud nlp libraries to
extract the document and paragraph level sentiment scores with of the
following article link -
https://www.fool.com/earnings/call-transcripts/2026/02/25/nvidia-nvda-q4-2026-earnings-call-transcript/
- the output should be in JSON format
- limit paragraph scores to first 10 paragraphs
Google will provide the code and a proper way to implement it on the cloud.
Results:
To fulfill your request, I have developed a Python solution that utilizes the
Google Cloud Natural Language API to perform sentiment analysis
on the document and the first 10 paragraphs of the Nvidia Q4 2026
earnings call transcript.
john_iacovacci1@cloudshell:~ (cloud-project-examples)$ mkdir prompt-eng
john_iacovacci1@cloudshell:~ (cloud-project-examples)$ cd prompt-eng/
john_iacovacci1@cloudshell:~/prompt-eng (cloud-project-examples)$
gcloud services enable language.googleapis.com
Note: Use your JSON key not mine
john_iacovacci1@cloudshell:~/prompt-eng (cloud-project-examples)$
export GOOGLE_APPLICATION_CREDENTIALS="/home/john_iacovacci1/cloud-project-examples-316c375c6892.json"
john_iacovacci1@cloudshell:~/prompt-eng (cloud-project-examples)$
pip install google-cloud-language beautifulsoup4 requests
Python code from gemini - sent_scores.py
========================================================
import json
from google.cloud import language_v1
import requests
from bs4 import BeautifulSoup
def analyze_sentiment(text_content):
"""Performs sentiment analysis using Google Cloud NLP."""
client = language_v1.LanguageServiceClient()
document = language_v1.Document(
content=text_content,
type_=language_v1.Document.Type.PLAIN_TEXT
)
# Extract Document and Sentence level sentiment
response = client.analyze_sentiment(request={'document': document})
return response
def get_transcript_paragraphs(url, limit=10):
"""Scrapes the transcript and extracts the first N paragraphs."""
headers = {'User-Agent': 'Mozilla/5.0'}
page = requests.get(url, headers=headers)
soup = BeautifulSoup(page.content, 'html.parser')
# Finding transcript text - typically within 'article-body' or specific divs
# This selector is optimized for the Motley Fool layout
content = soup.find('div', class_='article-body')
paragraphs = [p.get_text().strip() for p in content.find_all('p') if p.get_text().strip()]
return paragraphs[:limit]
def main():
url = "https://www.fool.com/earnings/call-transcripts/2026/02/25/nvidia-nvda-q4-2026-earnings-call-transcript/"
# 1. Extract content
paragraphs = get_transcript_paragraphs(url, limit=10)
full_text = "\n".join(paragraphs)
# 2. Call Google Cloud NLP
# Analyze the whole document (first 10 paragraphs combined)
doc_response = analyze_sentiment(full_text)
output = {
"document_sentiment": {
"score": round(doc_response.document_sentiment.score, 2),
"magnitude": round(doc_response.document_sentiment.magnitude, 2)
},
"paragraph_sentiments": []
}
# 3. Analyze each paragraph individually
for i, para in enumerate(paragraphs):
para_response = analyze_sentiment(para)
output["paragraph_sentiments"].append({
"paragraph_index": i + 1,
"text_snippet": para[:100] + "...",
"score": round(para_response.document_sentiment.score, 2),
"magnitude": round(para_response.document_sentiment.magnitude, 2)
})
# 4. Export to JSON
print(json.dumps(output, indent=4))
if __name__ == "__main__":
main()
========================================================
Results:
john_iacovacci1@cloudshell:~/prompt-eng (cloud-project-examples)$ python3 sent_scores.py
{
"document_sentiment": {
"score": 0.1,
"magnitude": 13.3
},
"paragraph_sentiments": [
{
"paragraph_index": 1,
"text_snippet": "Image source: The Motley Fool....",
"score": 0.1,
"magnitude": 0.1
},
{
"paragraph_index": 2,
"text_snippet": "Wednesday, Feb. 25, 2026 at 5 p.m. ET...",
"score": 0.0,
"magnitude": 0.0
},
{
"paragraph_index": 3,
"text_snippet": "Need a quote from a Motley Fool analyst? Email [email\u00a0protected]...",
"score": 0.0,
"magnitude": 0.2
},
{
"paragraph_index": 4,
"text_snippet": "The call revealed outsized growth in Data Center and Networking revenue underpinned by expanded cust...",
"score": 0.5,
"magnitude": 2.0
},
{
"paragraph_index": 5,
"text_snippet": "Toshiya Hari: Good afternoon, everyone, and welcome to NVIDIA Corporation's conference call for 2026...",
"score": -0.1,
"magnitude": 1.8
},
{
"paragraph_index": 6,
"text_snippet": "For a discussion of factors that could affect our future financial results and business, please refe...",
"score": -0.1,
"magnitude": 1.0
},
{
"paragraph_index": 7,
"text_snippet": "Colette Kress: Thanks, Toshiya. We delivered another outstanding quarter, with record revenue, opera...",
"score": 0.5,
"magnitude": 3.0
},
{
"paragraph_index": 8,
"text_snippet": "Agentic and physical AI applications built on increasingly smarter and multimodal models are beginni...",
"score": 0.4,
"magnitude": 0.4
},
{
"paragraph_index": 9,
"text_snippet": "On a full-year basis, Data Center generated revenue of $194,000,000,000, up 68% year over year. We h...",
"score": 0.0,
"magnitude": 1.8
},
{
"paragraph_index": 10,
"text_snippet": "SemiAnalysis declared NVIDIA Corporation inference king as recent results from InferenceX reinforced...",
"score": 0.7,
"magnitude": 2.9
}
]
}
john_iacovacci1@cloudshell:~/prompt-eng (cloud-project-examples)$
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