Global Unemployment Idea Theme Ignited S1 E1- Pilot mode
One of the best business choice, what we are facing issues globally and what we need will be world wide people get employment opportunities even without lengthy interview process in this era AI and Quantum technological changes, even Hiring recruitment will change people love to do the work, hence without talking in a meeting like environments People will get hired sooner than later choices & it will save time, human energy many aspects.
Recently created Global Unemployment Datasets and Notebook in kaggle environment
Here is the details
Prompt engineering is a critical component in the effective utilization of large language models (LLMs) like DeepSeek-R1, Gemma 2 2B, LLaMA 7B, and Qwen 2.5B. It involves designing prompts to guide the model in generating useful responses. Let's dive into some of the key techniques and how they can be applied to expand our dataset.
Multiple Draft Responses
Technique: Generate multiple responses for a given prompt and select the best one.
Application:
- Generate various narratives around each recession event in the dataset.
- Select the most informative and insightful responses for inclusion in the dataset.
Example:
Prompt: "Describe the key events during the 2008-2009 recession."
Multiple Drafts:
1. "The 2008-2009 recession, triggered by the subprime mortgage crisis, saw a collapse in housing prices, widespread foreclosures, and significant financial institution failures."
2. "During the 2008-2009 recession, governments worldwide implemented bank bailouts and stimulus packages to stabilize the economy, while unemployment rates soared."
3. "The subprime mortgage crisis in 2008 led to a global recession, characterized by plummeting housing markets, massive foreclosures, and extensive bank bailouts."
Zero-Shot Learning
Technique: The model generates responses without any task-specific training data.
Application:
- Ask the model to create new records in the dataset without providing examples.
- Useful for generating diverse responses based on minimal information.
Example:
Prompt: "Create a record for a hypothetical recession in 2030."
Zero-Shot Response:
```json
{
"id": 51,
"recession_timeline": "2030-2031",
"root_cause_recession": "Artificial Intelligence disruption in job markets",
"category": "recession",
"how_it_resolved": "Universal Basic Income implementation, retraining programs",
"key_insights": "Adapting to technological advancements and workforce evolution"
}
Few-Shot Learning
Technique: Provide a few examples to guide the model in generating responses.
Application:
- Use a few existing records to help the model generate new, similar records.
Example:
Prompt: "Based on the following examples, create a new record."
Examples:
1. "2020-2021, COVID-19 pandemic, recession, Government stimulus, vaccine distribution, Importance of rapid response and public health"
2. "2008-2009, Subprime mortgage crisis, great depression, Bank bailouts, regulatory reforms, Need for financial regulation and oversight"
Few-Shot Response:
```{
"id": 52,
"recession_timeline": "2035-2036",
"root_cause_recession": "Climate change-induced natural disasters",
"category": "recession",
"how_it_resolved": "Green energy investments, international cooperation",
"key_insights": "Importance of sustainable development and global cooperation"
}
```
Tree of Thoughts
Technique: Generate a tree of possible thoughts or steps to solve a problem, evaluate each branch, and select the most promising path.
Application:
- Create a tree of potential causes, resolutions, and insights for new recession events.
- Select the most logical and relevant pathways for dataset expansion.
Example:
Prompt: "Generate a tree of potential causes and resolutions for a 2040 recession."
Tree of Thoughts:
1. Cause: Financial market instability
- Resolution: Financial regulation reforms
- Key Insight: Importance of financial stability
2. Cause: Technological unemployment
- Resolution: Workforce retraining programs
- Key Insight: Adaptability to technological change
3. Cause: Global trade wars
- Resolution: Trade agreements and negotiations
- Key Insight: Impact of international relations on economies
Chain of Thought
Technique: Model generates a logical sequence of thoughts or steps leading to a final answer.
Application:
- Generate detailed narratives explaining the sequence of events during a recession.
- Useful for creating more comprehensive records.
Example:
Prompt: "Explain the sequence of events during the 1973-1975 recession."
Chain of Thought:
"The 1973-1975 recession was triggered by the oil embargo, leading to skyrocketing energy prices. As a result, inflation soared, and consumer spending plummeted. Businesses faced higher operational costs and reduced demand, leading to widespread layoffs. The government responded with energy policy reforms and monetary policy adjustments, eventually stabilizing the economy."
Scenario-Generated Conversations
Technique: Simulate conversations around hypothetical scenarios to explore various perspectives and outcomes.
Application:
- Generate conversational data between economists discussing potential future recessions.
- Provide diverse viewpoints and strategies for resolving economic crises.
Example:
Prompt: "Simulate a conversation between two economists discussing a 2050 recession."
Scenario-Generated Conversation:
Economist 1: "The 2050 recession seems to be driven primarily by the rapid advancements in quantum computing and its disruption of traditional industries."
Economist 2: "Agreed. The challenge will be in retraining the workforce to adapt to these new technologies and finding ways to integrate quantum computing into existing economic structures."
Economist 1: "Perhaps a focus on education and investment in new sectors could mitigate some of the impacts. What do you think about implementing a universal basic income?"
Economist 2: "It's definitely an option to consider, especially if automation continues to displace jobs. Balancing technological progress with social stability will be key."
Expanding Data Using LLMs
Using LLMs like DeepSeek-R1, Gemma 2 2B, LLaMA 7B, and Qwen 2.5B, we can further expand our dataset by:
1. Generating Hypothetical Scenarios: Create new recession scenarios based on current economic trends and potential future events.
2. Synthesizing Historical Data: Use LLMs to synthesize insights from historical data and create new records that highlight different aspects of past economic downturns.
3. Automating Data Augmentation: Develop automated pipelines to continuously generate and integrate new records into the dataset using prompt engineering techniques.
4. Diverse Perspectives: Generate data from multiple perspectives, including policymakers, business leaders, and economists, to provide a holistic view of economic crises and their resolutions.
By leveraging these advanced prompt engineering techniques, we can create a robust and comprehensive dataset that aids in understanding and mitigating the impacts of global economic downturns.
Agile user stories generated from LLM !!
Absolutely, let's create agile user stories specifically for recession dismissing scenarios using the Chain of Thoughts (CoT) and Tree of Thoughts (ToT) techniques.
Tree of Thoughts (ToT)
Goal: Develop features to implement and monitor strategies for dismissing recessions.
Tree of Thoughts:
1. User Story 1: Early Detection
- As a data scientist
- I want to analyze economic indicators to detect early signs of recession
- So that proactive measures can be taken to prevent the recession
2. User Story 2: Strategic Planning
- As a economic advisor
- I want to develop strategic plans based on historical data and predictive models
- So that we can effectively mitigate the impacts of potential recessions
3. User Story 3: Policy Implementation
- As a government official
- I want to implement targeted fiscal and monetary policies
- So that we can stabilize the economy and prevent recessions
4. User Story 4: Communication and Coordination
- As a policy maker
- I want to communicate and coordinate with international economic bodies
- So that we can collaborate on global economic stability measures
5. User Story 5: Public Awareness
- As a communication specialist
- I want to inform the public about the measures being taken
- So that we can maintain public confidence and prevent panic
Expanded Tree of Thoughts:
- For Early Detection:
- Sub-task: Develop algorithms to monitor economic indicators
- Sub-task: Integrate real-time data feeds
- Sub-task: Set up alert systems for early warning signs
- For Strategic Planning:
- Sub-task: Analyze historical data for patterns and trends
- Sub-task: Build predictive models using machine learning
- Sub-task: Develop contingency plans for various recession scenarios
- For Policy Implementation:
- Sub-task: Formulate fiscal policies such as tax cuts or increased public spending
- Sub-task: Adjust monetary policies such as interest rate changes
- Sub-task: Monitor the impact of implemented policies
Chain of Thoughts (CoT)
Goal: Develop a user-friendly platform to execute and monitor recession-prevention strategies.
Chain of Thoughts:
1. User Story 1: Economic Indicator Dashboard
- As a user
- I want to view a dashboard of key economic indicators
- So that I can monitor real-time data for early signs of recession
2. User Story 2: Predictive Analytics Tool
- As a user
- I want to access a predictive analytics tool
- So that I can see forecasts and model outcomes for recession scenarios
3. User Story 3: Policy Simulation
- As a user
- I want to simulate various fiscal and monetary policies
- So that I can assess their potential impact on preventing recessions
4. User Story 4: Collaborative Platform
- As a user
- I want to collaborate with other stakeholders on the platform
- So that we can coordinate efforts to prevent recessions
5. User Story 5: Public Information Portal
- As a user
- I want to access a public information portal
- So that I can stay informed about government measures and economic stability
Detailed Chain of Thoughts:
- For Economic Indicator Dashboard:
- Step 1: Design a clean and informative dashboard layout
- Step 2: Integrate real-time data feeds from reliable sources
- Step 3: Allow users to customize the dashboard view and set alerts
- For Predictive Analytics Tool:
- Step 1: Develop machine learning models to predict recession scenarios
- Step 2: Integrate the models into the platform
- Step 3: Provide users with interactive tools to explore model outputs
- For Policy Simulation:
- Step 1: Create simulation models for various fiscal and monetary policies
- Step 2: Allow users to input different policy parameters
- Step 3: Visualize the potential impact of each policy scenario
By using CoT and ToT techniques, we can develop a comprehensive set of user stories that focus on proactive recession prevention and management. These user stories will help guide the development of features and functionalities that enable stakeholders to monitor, predict, and mitigate economic downturns effectively.