Example 2
This technique does seem to have a dramatic and direct impact on th eoutput of a Task.
To demonstrate: I have added another new field to the model: geo_regions. The description of geo_regions is the only place where the summary of geo regions is mentioned.
The updated StageOneOutputModel:
class Stage1OutputModel(BaseModel):
summary: Optional[str] = Field("", description="Explanation of how the report should be read")
bullet_points: Optional[List[str]] = Field([], description="3 key bullet points from the report")
todo: Optional[str] = Field("", description="Suggestions for further research")
geo_regions: Optional[List[str]] = Field([], description="Highlight geographical regions where research is most active")
def __init__(self):
super().__init__()
def get_field_info_json(self) -> str:
field_info = "\n"
for field_name, field_instance in self.model_fields.items():
field_info += field_name + ", described as: " + field_instance.description + "\n"
return field_info
The updated reporting_task description that references the new model fields todo & geolocations.:
description="""
Review the summary you got and expand each bullet-point into a full section for a report.
Make sure the report is detailed and contains any and all relevant information.
Mention any further research todo and an overview of geographic hot-spots.
""",
Updating the above gave this output from the research_task:
2024-09-14 18:58:46][DEBUG]: == [AI LLMs Senior Data Researcher
] Task output: {
"summary": "This report provides a comprehensive overview of the latest developments in AI Language Learning Models (LLMs) as of 2024. It includes updated statistics, real-world applications, and case studies that demonstrate the practical uses of AI LLMs across various industries. The report also highlights key geographical regions leading in this technology's research and development, along with suggestions for visual elements that could enhance understanding. The information is structured to facilitate easy navigation and comprehension.",
"bullet_points": [
"Statistics Overview: As of 2024, the global AI LLM market is projected to reach $45 billion, reflecting a growth rate of 25% annually. Over 70% of enterprises report deploying LLMs in at least one of their operations.",
"Real-World Applications: Industries such as healthcare, finance, and education are leveraging AI LLMs for tasks ranging from patient interaction and fraud detection to personalized learning experiences, demonstrating their versatility.",
"Geographical Insights: North America, particularly the United States, remains the leader in AI LLM research, followed closely by Europe and Asia, with countries like Canada, Germany, and China showing significant advancements and investments in AI technologies."
],
"todo": [
"Explore case studies from specific industries (e.g., healthcare and finance) to better understand the impact of AI LLMs.",
"Research emerging trends in AI ethics related to LLMs, particularly concerning data privacy and bias mitigation.",
"Investigate the regulatory landscape in different regions concerning AI LLM deployment."
],
"geo_regions": [
"North America: The United States is the hub for AI LLM research, with prominent tech companies and universities leading innovations.",
"Europe: Countries like Germany and the United Kingdom are making significant contributions, focusing on ethical AI development.",
"Asia: China's rapid investment in AI technologies and its integration of LLMs into government services and e-commerce platforms shows a strong commitment to advancing this field."
]
}
The newly referenced model fields were seen in the final output:
> Finished chain.
[2024-09-14 18:59:04][DEBUG]: == [AI LLMs Reporting Analyst
] Task output: # Comprehensive Report on AI Language Learning Models (LLMs) as of 2024
This report provides a comprehensive overview of the latest developments in AI Language Learning Models (LLMs) as of 2024. It includes updated statistics, real-world applications, and case studies that demonstrate the practical uses of AI LLMs across various industries. The report also highlights key geographical regions leading in this technology's research and development, along with suggestions for visual elements that could enhance understanding. The information is structured to facilitate easy navigation and comprehension.
## 1. Statistics Overview
As of 2024, the global market for AI Language Learning Models (LLMs) is projected to reach an impressive **$45 billion**, marking a remarkable growth trajectory with an annual increase rate of **25%**. This robust expansion is indicative of the increasing reliance on AI technologies across various sectors. In fact, more than **70%** of enterprises have reported implementing LLMs in at least one operational facet, ranging from customer service automation to content generation.
This widespread adoption not only underscores the efficiency that LLMs bring to business processes but also highlights a significant shift towards data-driven decision-making and enhanced user experiences. The proliferation of LLMs is transforming workforce dynamics, creating new job roles while also necessitating upskilling and training for existing employees to effectively harness the capabilities of these advanced models.
## 2. Real-World Applications
The versatility of AI LLMs is vividly illustrated across multiple industries, with notable applications in **healthcare**, **finance**, and **education**.
In **healthcare**, LLMs are revolutionizing patient interaction through chatbots and virtual assistants that provide timely medical advice, schedule appointments, and manage patient records, thereby enhancing patient engagement and operational efficiency.
The **finance** sector utilizes LLMs for sophisticated fraud detection mechanisms, analyzing transaction patterns, and identifying anomalies that indicate fraudulent activities.
Furthermore, in the realm of **education**, LLMs enable personalized learning experiences by tailoring educational content to individual student needs and learning paces, thus enhancing educational outcomes and engagement.
These real-world applications demonstrate how LLMs are not merely technological innovations but critical tools that drive productivity and improve service delivery across diverse fields.
## 3. Geographical Insights
Geographically, **North America**, specifically the **United States**, continues to lead the charge in AI LLM research and development. The U.S. boasts a rich ecosystem of tech companies, startups, and academic institutions that drive innovation in this field.
Following closely, **Europe** and **Asia** are also emerging as significant players, with countries such as **Canada**, **Germany**, and **China** making substantial investments in AI technologies. In Canada, for instance, the emphasis on ethical AI development complements its advancements in LLM research. Germany's strong industrial base has led to practical applications of LLMs in manufacturing and logistics. Meanwhile, China has rapidly scaled its AI capabilities, focusing on integrating LLMs into its vast data infrastructure.
The collaborative efforts and competitive spirit in these regions are propelling AI LLMs to the forefront of technological advancement, positioning them as critical drivers of economic growth in the years to come.
## Research Directions
To further enrich this report, it is crucial to explore specific **case studies** from industries like healthcare and finance to elucidate the impact of AI LLMs in real-world scenarios. Such case studies will provide insights into the tangible benefits and challenges faced by organizations during implementation.
Additionally, probing emerging trends in **AI ethics** related to LLMs is vital, particularly in areas concerning **data privacy** and **bias mitigation**. Understanding how organizations are addressing these ethical challenges will shed light on the responsible deployment of AI technologies.
Furthermore, investigating the **regulatory landscape** across different regions regarding AI LLM deployment will offer valuable context on the governance frameworks that shape the development and application of these models.
As we identify geographical hotspots such as North America, Europe, and Asia, detailing their contributions and advances in AI LLMs will provide a comprehensive understanding of the global landscape and future directions in this exciting field.
In summary, this report aims to present a thorough overview of the state of AI LLMs as of 2024, highlighting the dynamic developments, practical applications, and geographical contributions that define this transformative technology.
**REM
Class fields can be dynamically added, removed & mutated at run time 
I’m a ‘noob’ at this, there must be a better way of doing the same! The challenge is issued 
Maybe not!, Maybe so! 