What is Chain-of-Thought (CoT) Prompting?
Large Language Models (LLMs) demonstrate remarkable capabilities in natural language processing (NLP) and generation. However, when faced with complex reasoning tasks, these models can struggle to produce accurate and reliable results. This is where Chain-of-Thought...
AI Research Paper Summarized: “Chain of Thought(lessness)?” Prompting
Chain-of-Thought (CoT) prompting has been hailed as a breakthrough in unlocking the reasoning capabilities of large language models (LLMs). This technique, which involves providing step-by-step reasoning examples to guide LLMs, has garnered significant attention in...
Top 10 LLM Prompting Techniques for Maximizing AI Performance
The art of crafting effective large language model (LLM) prompts has become a crucial skill for AI practitioners. Well-designed prompts can significantly enhance an LLM's performance, enabling more accurate, relevant, and creative outputs. This blog post explores ten...
What is Few Shot Learning?
In AI, the ability to learn efficiently from limited data has become crucial. Enter Few Shot Learning, an approach that's improving how AI models acquire knowledge and adapt to new tasks. But what exactly is Few Shot Learning? Defining Few Shot Learning Few Shot...
Few-Shot Prompting vs Fine-Tuning LLM for Generative AI Solutions
The true potential of large language models (LLMs) lies not just in their vast knowledge base, but in their ability to adapt to specific tasks and domains with minimal additional training. This is where the concepts of few-shot prompting and fine-tuning come into...
Top 5 Research Papers on Few-Shot Learning
Few-shot learning has emerged as a crucial area of research in machine learning, aiming to develop algorithms that can learn from limited labeled examples. This capability is essential for many real-world applications where data is scarce, expensive, or time-consuming...
Should Your Enterprise Consider Llama 3.1? – AI&YOU #66
Stat of the Week: 72% of surveyed organizations have adopted AI in 2024, a significant jump from around 50% in previous years. (McKinsey) Meta's recent release of Llama 3.1 has sent ripples through the enterprise world. This latest iteration of the Llama models...
10 Proven Strategies to Cut Your LLM Costs – AI&YOU #65
Stat of the Week: Using smaller LLMs like GPT-J in a cascade can reduce overall cost by 80% while improving accuracy by 1.5% compared to GPT-4. (Dataiku) As organizations increasingly rely on large language models (LLMs) for various applications, the operational costs...
10 Proven Strategies to Cut Your LLM Costs
As organizations increasingly rely on large language models (LLMs) for various applications, from customer service chatbots to content generation, the challenge of LLM cost management has come to the forefront. The operational costs associated with deploying and...
Understanding LLM Pricing Structures: Inputs, Outputs, and Context Windows
For enterprise AI strategies, understanding large language model (LLM) pricing structures is crucial for effective cost management. The operational costs associated with LLMs can quickly escalate without proper oversight, potentially leading to unexpected cost spikes...
Meta’s Llama 3.1: Pushing the Boundaries of Open-Source AI
Meta has recently announced Llama 3.1, its most advanced open-source large language model (LLM) to date. This release marks a significant milestone in the democratization of AI technology, potentially bridging the gap between open-source and proprietary models. Llama...
Should your Enterprise Use Llama 3.1?
Meta's recent release of Llama 3.1 has sent ripples through the enterprise world. This latest iteration of the Llama models represents a significant leap forward in the realm of large language models (LLMs), offering a blend of performance and accessibility that...
Llama 3.1 vs. Proprietary LLMs: A Cost-Benefit Analysis for Enterprises
The landscape of large language models (LLMs) has become a battleground between open-weight models like Meta's Llama 3.1 and proprietary offerings from tech giants like OpenAI. As enterprises navigate this complex terrain, the decision between adopting an open model...
10 Reasons your Enterprise Should Use Llama 3.1
Meta's Llama 3.1 has emerged as an impressive LLM option, offering a unique blend of performance, flexibility, and cost-effectiveness. As enterprises navigate the complex world of AI implementation, Llama 3.1 presents compelling reasons for serious consideration....
How a Marketer Can Optimize Content for Perplexity AI + Breaking Down Copyright Controversies – AI&YOU #62
Stat of the Week: In May 2024, Perplexity AI received 67.42 million visits with an average session duration of 10 minutes 51 seconds. Traffic increased by 20.71% compared to April. (Semrush) In digital marketing, staying ahead is crucial. As online research evolves,...
AI Research Paper Breakdown for ChainPoll: A High Efficacy Method for LLM Hallucination Detection
In this article, we are going to break down an important research paper that addresses one of the most pressing challenges facing large language models (LLMs): hallucinations. The paper, titled "ChainPoll: A High Efficacy Method for LLM Hallucination Detection,"...
How Enterprises can Tackle LLM Hallucinations to Safely Integrate AI
Large language models (LLMs) are transforming enterprise applications, offering unprecedented capabilities in natural language processing and generation. However, before your enterprise jumps on the LLM bandwagon, there's a critical challenge you need to address:...
Top 10 Ways to Eliminate LLM Hallucinations
As large language models (LLMs) continue to disrupt nearly every field and industry, they bring with them a unique challenge: hallucinations. These AI-generated inaccuracies pose a significant risk to the reliability and trustworthiness of LLM outputs. What are LLM...
Top 5 Platforms for Building AI Agents
AI agents are autonomous software entities designed to perform complex tasks and make decisions with minimal human intervention. As enterprises increasingly recognize the potential of these intelligent systems, the demand for robust platforms capable of building AI...
How AgentOps Helps in Managing LLM Costs
As AI agents become increasingly prevalent in enterprise solutions, the management of Large Language Model (LLM) costs has emerged as a critical concern for developers and businesses alike. LLMs, while powerful, can be expensive to operate, especially at scale. The...
How AgentOps Helps Developers Build and Monitor Reliable AI Agents
As AI agents grow in sophistication, developers face significant challenges in ensuring their reliability, performance, and cost-effectiveness. The development and monitoring of AI agents present unique hurdles, including: Managing the intricacies of multi-agent...
How Marketers Can Optimize Content for Perplexity AI
Staying ahead of the curve is a necessity in digital marketing. As the landscape of online research continues to evolve, marketers are constantly seeking more efficient and effective ways to gather insights, generate ideas, and make data-driven decisions. Perplexity...
10 Things to Know About the Perplexity Copyright Controversy
Perplexity AI has emerged as a disruptive force in the search engine market. This innovative AI-powered answer engine promises to revolutionize how we access and interact with online content. However, recent controversies have thrust Perplexity into the spotlight,...
What is Perplexity Pages?
Perplexity Pages is an innovative tool developed by Perplexity AI that aims to redefine the boundaries between search engines, research platforms, and content management systems. It is generating buzz for its potential to create visually appealing articles and...
Ready to grow your business with AI? Get in touch
Call
English 
Español 
Français 
Deutsch 
Italiano 
Português 
日本語 
한국어 
Українська