Category: LLM
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From Static Scripts to Smart Agents: A New Era in Financial Analysis
Static financial scripts create bottlenecks that can’t keep pace with the market. This post demonstrates a more powerful approach: building an AI agent that understands natural language. It details a method to transform rigid code into a dynamic analytical engine that generates practical, interactive dashboards from simple, conversational commands.
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From Monolithic Models to AI Agents: A New Era of Generative AI
Large Language Models (LLMs) exhibit impressive capabilities but are susceptible to hallucinations. To address this issue, agentic AI emerges as a potential solution. The shift from monolithic models to compound AI systems, along with the rise of AI agents, represents significant progress in generative AI and problem-solving capacity. In this post, we will share key…
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Comparing Retrieval-Augmented Generation (RAG) and Fine-tuning: Advantages and Limitations
Analyzing Retrieval-Augmented Generation and fine-tuning in AI, this blog post examines their distinct advantages and challenges. Comparing these methods helps identify the ideal approach for specific project needs and objectives.
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The Perils of Progress: When AI Gets History and Language Wrong
Large Language Models (LLMs) like those powering chatbots such as Gemini and ChatGPT are demonstrably effective at tasks like text generation and translation. However, recent incidents expose vulnerabilities. This blog post examines these incidents, stressing the crucial need for responsible development and deployment of LLMs.
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BloombergGPT: An Overview of a Language Model Tailored for Finance
While general purpose large language models (LLMs) like GPT-3 are incredibly versatile, their performance on domain-specific tasks is often less impressive. This blog post will delve into the development and performance of BloombergGPT and its significance for the finance industry.
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